To get started, can you confirm that you have access to the Encoding Music MCP server? What can it help me to do?
Checking access to the music encoding tool.
Recalled memory · encoding-music-mcp.md
Request and response response (6.0 k chars)
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… [truncated, 17445 characters in total]
Preparing a concise, practical summary of the counts.
Yes — the server is live and responding. list_available_mei_files came back with the full built-in corpus: 15 Bach Two-Part Inventions, 19 Bartók Mikrokosmos pieces, the 12 Morley 1595 canzonets, and the CRIM Renaissance set (Mass movements plus 52 models). No uploaded files in the session yet.
What the 21 tools cover:
Getting at the data — list_available_mei_files, get_mei_metadata for header info (title, composer, editors, analysts), and register_mei_file_from_path if you want to point it at a local MEI file instead.
Extraction via CRIM Intervals — notes, melodic intervals (diatonic, chromatic, with-quality, or zero-based), harmonic intervals by voice pair. All returned as CSV-shaped dataframes indexed by measure/beat.
Pattern work — melodic n-grams in four flavours: the raw table, frequency-ranked counts, first occurrence of each unique pattern, and occurrence-level matches grouped by pattern with note IDs attached. Entry filtering (n-grams after rests, section breaks, or fermatas) is there for isolating thematic material.
Analysis — analyze_key (music21's algorithm, with a confidence correlation) and get_cadences (CRIM's cadence prediction, with type, tone, and cadential voice functions).
Display and audio — paged Verovio notation rendering, the same with arbitrary note IDs highlighted, voice-range plots, duration-weighted pitch-class radar charts, melodic n-gram heatmaps laid out per staff over time, sonority n-grams plotted by normalised progress through a score, and MP3 rendering of excerpts at a chosen tempo and quarter-note range.
The bit that ties it together is resolve_note_ids_for_highlight — it turns any analysis location (a cadence row, an n-gram span, a harmonic interval position) into MEI note IDs, so anything you find in a table can be pushed straight back onto the notation.
Want me to run something end-to-end on a piece as a check?
Let's dig further into the data first. Can you list out the file names and titles of the Bach inventions provided?
Request and response request · response (0.7 k chars)
{
"filename": "Bach_BWV_0772.mei"
}
{
"title": "Invention No. 1 in C major, BWV 772",
"composer": "Bach, Johann Sebastian",
"mei_editors": [
"Freedman, Richard"
],
"xml_editors": [
"Schölkopf, Tobias"
],
"analysts": [
"Student, This"
],
"publication_date": "2024-11-19",
"availability": "The content and works published on this website are governed by the copyright laws of Germany. Any duplication, processing, distribution or any form of utilisation beyond the scope of copyright law shall require the prior written consent of the author or authors in question.",
"application": "MEI Soup Updater 2024",
"work_title": "Invention No. 1 in C major"
}
All fifteen inventions, in BWV order:
| Filename | Title |
|---|---|
| Bach_BWV_0772.mei | Invention No. 1 in C major, BWV 772 |
| Bach_BWV_0773.mei | Invention No. 2 in C minor, BWV 773 |
| Bach_BWV_0774.mei | Invention No. 3 in D major, BWV 774 |
| Bach_BWV_0775.mei | Invention No. 4 in D minor, BWV 775 |
| Bach_BWV_0776.mei | Invention No. 5 in E-flat major, BWV 776 |
| Bach_BWV_0777.mei | Invention No. 6 in E major, BWV 777 |
| Bach_BWV_0778.mei | Invention No. 7 in E minor, BWV 778 |
| Bach_BWV_0779.mei | Invention No. 8 in F major, BWV 779 |
| Bach_BWV_0780.mei | Invention No. 9 in F minor, BWV 780 |
| Bach_BWV_0781.mei | Invention No. 10 in G major, BWV 781 |
| Bach_BWV_0782.mei | Invention No. 11 in G minor, BWV 782 |
| Bach_BWV_0783.mei | Invention No. 12 in A major, BWV 783 |
| Bach_BWV_0784.mei | Invention No. 13 in A minor, BWV 784 |
| Bach_BWV_0785.mei | Invention No. 14 in B-flat major, BWV 785 |
| Bach_BWV_0786.mei | Invention No. 15 in B minor, BWV 786 |
The complete set, no gaps. All fifteen share the same provenance: MEI editing by Richard Freedman, XML editing by Tobias Schölkopf, published 2024-11-19 via "MEI Soup Updater 2024". Each has a different named analyst in the header — student encodings, by the look of it — except BWV 781, where the analyst field is an empty string rather than absent. Worth knowing if you're ever filtering or grouping on that field.
Bring up the notation and audio of BWV 772
Confirming permission to use the named connector.
Notation is paged — seven pages in the viewer, so you can step through the whole piece there. Audio is the full invention at 76 bpm; say the word if you'd rather hear it slower or just a specific measure range.
Can you identify the melodic patterns that recur throughout this invention?
Request and response request · response (6.0 k chars)
{
"filename": "Bach_BWV_0772.mei",
"kind": "d",
"n": 4
}
{"filename":"Bach_BWV_0772.mei","n":4,"kind":"d","entries":false,"combine_unisons":null,"compound":false,"pattern_counts":[{"pattern":["-2","3","-2","3"],"pattern_string":"-2_3_-2_3","count":27},{"pattern":["3","-2","3","-2"],"pattern_string":"3_-2_3_-2","count":27},{"pattern":["-2","-2","3","-2"],"pattern_string":"-2_-2_3_-2","count":21},{"pattern":["-2","-2","-2","3"],"pattern_string":"-2_-2_-2_3","count":20},{"pattern":["2","-3","2","-3"],"pattern_string":"2_-3_2_-3","count":20},{"pattern":["2","2","2","-3"],"pattern_string":"2_2_2_-3","count":20},{"pattern":["2","2","-3","2"],"pattern_string":"2_2_-3_2","count":19},{"pattern":["-2","-2","-2","-2"],"pattern_string":"-2_-2_-2_-2","count":17},{"pattern":["-2","3","-2","-2"],"pattern_string":"-2_3_-2_-2","count":10},{"pattern":["-3","2","-3","2"],"pattern_string":"-3_2_-3_2","count":10},{"pattern":["2","2","2","2"],"pattern_string":"2_2_2_2","count":10},{"pattern":["3","-2","-2","-2"],"pattern_string":"3_-2_-2_-2","count":8},{"pattern":["-3","2","2","2"],"pattern_string":"-3_2_2_2","count":7},{"pattern":["2","-3","2","2"],"pattern_string":"2_-3_2_2","count":7},{"pattern":["-3","2","-3","5"],"pattern_string":"-3_2_-3_5","count":5},{"pattern":["-6","2","2","2"],"pattern_string":"-6_2_2_2","count":5},{"pattern":["-3","5","4","-2"],"pattern_string":"-3_5_4_-2","count":4},{"pattern":["2","-3","5","4"],"pattern_string":"2_-3_5_4","count":4},{"pattern":["2","-6","2","2"],"pattern_string":"2_-6_2_2","count":4},{"pattern":["2","2","-6","2"],"pattern_string":"2_2_-6_2","count":4},{"pattern":["2","2","2","-6"],"pattern_string":"2_2_2_-6","count":4},{"pattern":["5","4","-2","2"],"pattern_string":"5_4_-2_2","count":4},{"pattern":["-2","-3","2","2"],"pattern_string":"-2_-3_2_2","count":3},{"pattern":["-2","3","-2","-3"],"pattern_string":"-2_3_-2_-3","count":3},{"pattern":["-2","3","-2","-4"],"pattern_string":"-2_3_-2_-4","count":3},{"pattern":["-2","3","-2","-5"],"pattern_string":"-2_3_-2_-5","count":3},{"pattern":["-3","2","-3","4"],"pattern_string":"-3_2_-3_4","count":3},{"pattern":["3","-2","-2","2"],"pattern_string":"3_-2_-2_2","count":3},{"pattern":["3","-2","-3","2"],"pattern_string":"3_-2_-3_2","count":3},{"pattern":["3","-2","-4","2"],"pattern_string":"3_-2_-4_2","count":3},{"pattern":["4","-2","-2","-2"],"pattern_string":"4_-2_-2_-2","count":3},{"pattern":["4","-2","2","2"],"pattern_string":"4_-2_2_2","count":3},{"pattern":["-2","-2","-2","6"],"pattern_string":"-2_-2_-2_6","count":2},{"pattern":["-2","-2","2","-3"],"pattern_string":"-2_-2_2_-3","count":2},{"pattern":["-2","-2","6","-2"],"pattern_string":"-2_-2_6_-2","count":2},{"pattern":["-2","-4","2","2"],"pattern_string":"-2_-4_2_2","count":2},{"pattern":["-2","-5","7","2"],"pattern_string":"-2_-5_7_2","count":2},{"pattern":["-2","2","-3","2"],"pattern_string":"-2_2_-3_2","count":2},{"pattern":["-2","2","2","-5"],"pattern_string":"-2_2_2_-5","count":2},{"pattern":["-2","2","2","2"],"pattern_string":"-2_2_2_2","count":2},{"pattern":["-2","6","-2","-2"],"pattern_string":"-2_6_-2_-2","count":2},{"pattern":["-3","-2","-2","-2"],"pattern_string":"-3_-2_-2_-2","count":2},{"pattern":["-3","2","2","-2"],"pattern_string":"-3_2_2_-2","count":2},{"pattern":["-3","2","4","-2"],"pattern_string":"-3_2_4_-2","count":2},{"pattern":["-4","2","2","2"],"pattern_string":"-4_2_2_2","count":2},{"pattern":["-5","2","2","2"],"pattern_string":"-5_2_2_2","count":2},{"pattern":["-5","7","2","-3"],"pattern_string":"-5_7_2_-3","count":2},{"pattern":["-7","2","2","2"],"pattern_string":"-7_2_2_2","count":2},{"pattern":["1","-7","2","2"],"pattern_string":"1_-7_2_2","count":2},{"pattern":["2","-2","-2","-2"],"pattern_string":"2_-2_-2_-2","count":2},{"pattern":["2","-3","-2","-2"],"pattern_string":"2_-3_-2_-2","count":2},{"pattern":["2","-3","2","4"],"pattern_string":"2_-3_2_4","count":2},{"pattern":["2","-3","4","-2"],"pattern_string":"2_-3_4_-2","count":2},{"pattern":["2","-5","2","2"],"pattern_string":"2_-5_2_2","count":2},{"pattern":["2","1","-7","2"],"pattern_string":"2_1_-7_2","count":2},{"pattern":["2","2","-2","-2"],"pattern_string":"2_2_-2_-2","count":2},{"pattern":["2","2","-5","2"],"pattern_string":"2_2_-5_2","count":2},{"pattern":["2","2","1","-7"],"pattern_string":"2_2_1_-7","count":2},{"pattern":["2","2","2","1"],"pattern_string":"2_2_2_1","count":2},{"pattern":["2","4","-2","-2"],"pattern_string":"2_4_-2_-2","count":2},{"pattern":["3","-2","-5","7"],"pattern_string":"3_-2_-5_7","count":2},{"pattern":["7","2","-3","-2"],"pattern_string":"7_2_-3_-2","count":2},{"pattern":["8","-2","-2","-2"],"pattern_string":"8_-2_-2_-2","count":2},{"pattern":["-2","-2","-2","1"],"pattern_string":"-2_-2_-2_1","count":1},{"pattern":["-2","-2","1","-2"],"pattern_string":"-2_-2_1_-2","count":1},{"pattern":["-2","-2","1","8"],"pattern_string":"-2_-2_1_8","count":1},{"pattern":["-2","-2","2","-9"],"pattern_string":"-2_-2_2_-9","count":1},{"pattern":["-2","-2","2","2"],"pattern_string":"-2_-2_2_2","count":1},{"pattern":["-2","-4","2","-3"],"pattern_str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… [truncated, 9669 characters in total]
Analyzing the melodic structure of a musical subject.
Request and response request · response (0.5 k chars)
{
"entries": true,
"filename": "Bach_BWV_0772.mei",
"kind": "d",
"n": 6
}
{
"filename": "Bach_BWV_0772.mei",
"n": 6,
"kind": "d",
"entries": true,
"combine_unisons": null,
"compound": false,
"pattern_counts": [
{
"pattern": [
"2",
"2",
"2",
"-3",
"2",
"-3"
],
"pattern_string": "2_2_2_-3_2_-3",
"count": 6
},
{
"pattern": [
"-2",
"-2",
"-2",
"3",
"-2",
"3"
],
"pattern_string": "-2_-2_-2_3_-2_3",
"count": 3
}
]
}
Request and response request · response (6.0 k chars)
{
"filename": "Bach_BWV_0772.mei",
"kind": "d",
"n": 6,
"patterns": [
"2_2_2_-3_2_-3",
"-2_-2_-2_3_-2_3"
]
}
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… [truncated, 8853 characters in total]
Compiling note IDs for every subject and inversion occurrence.
Analyzing motif patterns and structure across the musical piece.
The invention is built almost entirely from one idea. Entry-filtered n-grams (patterns that begin after a rest or break) return exactly two results, which is about as clean a signature as you get:
2_2_2_-3_2_-3— the subject: up a 2nd three times, down a 3rd, up a 2nd, down a 3rd. That's C–D–E–F–D–E–C at the opening. 6 entries, 18 occurrences in total.-2_-2_-2_3_-2_3— its exact melodic inversion. 3 entries, 19 occurrences.
Here's where the prime form falls:
Note how the entries alternate strictly between the two hands, half a bar apart, in mm. 1–2, then again at 7–8, 16, 18–20. And the inverted form:
Between them, the two forms account for 37 statements in a 22-bar piece. The prime dominates the opening and the return (mm. 1–2, 7–8, 16–20); the inversion takes over almost completely in the middle, running continuously from m. 9 to m. 13, and it first appears as early as m. 3 in the right hand — before the piece has even left the tonic properly.
Below that level, the frequent n=4 patterns are mostly just fragments of the same thing:
| Pattern | Count | What it is |
|---|---|---|
3_-2_3_-2 / -2_3_-2_3 |
27 each | the subject's tail, sequenced |
-2_-2_3_-2, -2_-2_-2_3 |
21, 20 | prime/inversion overlaps |
2_-3_2_-3, 2_2_2_-3 |
20, 20 | same, prime form |
-2_-2_-2_-2 / 2_2_2_2 |
17, 10 | plain scalar runs — episode material |
One genuinely separate idea does show up: the chain 2_-3_5_4 → -3_5_4_-2 → 5_4_-2_2, four or five occurrences each. That leap-containing figure isn't derivable from the subject by inversion, so it's likely the eighth-note counter-material rather than more of the same. Happy to pull its locations and highlight it too, or to run the n-gram heatmap so you can see prime and inversion distributed across both staves over the whole timeline at once.
It'd be great to see these patterns on a heatmap for the piece
Setting up a melodic pattern heatmap analysis.
Request and response request · response (6.0 k chars)
{
"entries": false,
"filename": "Bach_BWV_0772.mei",
"kind": "d",
"n": 6
}
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… [truncated, 16380 characters in total]
Searching for the best-scoring sequence at this length.
The four rows plotted are, by raw count at n=6:
- -2_-2_-2_3_-2_3 — 19 — the inversion
- -2_-2_3_-2_3_-2 — 19 — the same inversion, window shifted one note later
- 2_2_2_-3_2_-3 — 18 — the prime subject
- -2_-2_-2_-2_3_-2 — 9 — inversion preceded by an extra descending step
Which is itself a useful thing to notice: three of the top four are the inverted subject caught at different phase offsets. Counting sliding windows means one musical event gets tallied under several pattern labels, so raw rank overstates how many distinct ideas are present. If you're using this in a workshop, that's a nice teachable artefact — the tool is right and the interpretation is the hard part.
Reading across the timeline, the visual confirms what the note-ID locations showed: prime clusters at the two ends, inversion owns the middle, and the two staves trade the material rather than doubling it — almost no vertical overlap of the same pattern in both rows at once.
If you want a cleaner picture, I'd re-run it at n=8 or with entries=true, which collapses the phase-shifted duplicates and leaves you with just the two genuine forms.
Wikidata
Can you use the Wikidata MCP to find the places associated with Bach's life and career, and then the Mapbox MCP to chart them, albeit on a modern basemap?
Request and response request · response (0.5 k chars)
{
"K": 20,
"sparql": "SELECT ?relLabel ?place ?placeLabel ?coord WHERE { VALUES (?p ?rel) { (wdt:P19 \"birth\") (wdt:P20 \"death\") (wdt:P119 \"burial\") (wdt:P937 \"work location\") } wd:Q1339 ?p ?place . ?place wdt:P625 ?coord . SERVICE wikibase:label { bd:serviceParam wikibase:language \"[AUTO_LANGUAGE],en,mul\". } }"
}
;place;coord;relLabel;placeLabel 0;Q2079;Point(12.3747329 51.3406321);death;Leipzig 1;Q2079;Point(12.3747329 51.3406321);work location;Leipzig 2;Q3955;Point(11.329444444 50.981111111);work location;Weimar 3;Q7070;Point(10.324444444 50.974722222);birth;Eisenach 4;Q14925;Point(10.45 51.216666666);work location;Mühlhausen/Thuringia 5;Q170402;Point(12.372586 51.339292);burial;St. Thomas Church 6;Q490665;Point(10.946388888 50.834166666);work location;Arnstadt 7;Q1796771;Point(11.973611111 51.751111111);work location;Köthen
Compiling missing biographical and educational details.
Request and response request · response
{
"K": 10,
"sparql": "SELECT ?placeLabel ?coord WHERE { VALUES ?place { wd:Q1930161 wd:Q798194 wd:Q699668 } OPTIONAL { ?place wdt:P625 ?coord } SERVICE wikibase:label { bd:serviceParam wikibase:language \"[AUTO_LANGUAGE],en,mul\". } }"
}
;coord;placeLabel 0;Point(12.3599 51.337);Thomasschule zu Leipzig 1;Point(10.946436236 50.8342521);Bachkirche Arnstadt 2;;St. Michael's School
Setting up a map using the requested tool.
Reconstructing a career timeline from scattered records.
Numbered in career order — that sequencing is mine, not Wikidata's, since work location (P937) is an unordered set with no date qualifiers on this item. The orange pins are the two Leipzig institutions.
Two gaps worth knowing about:
St Michael's School, Lüneburg — the educated at value — has no coordinate statement at all, so it can't be plotted. That's the northern outlier of Bach's biography missing from the map. Ohrdruf, where he lived with his elder brother after their parents died, isn't recorded on Q1339 in any property, so it doesn't even surface as a gap.
The striking thing once it's on a basemap: the entire documented career sits inside a box roughly 145 km east–west and 100 km north–south. Everything except Lüneburg is within a day or two's travel of everything else, in present-day Thuringia and Saxony-Anhalt. The Köthen post is the only real northward excursion, and it's the one that produced the instrumental repertoire — the Inventions among it.
If it's useful, I could add distance_tool or matrix_tool measurements between the posts, or pull the same treatment for the Morley and CRIM composers to compare geographic footprints.
World Bank
Let's pivot to a different topic and try out the World Bank MCP. Can you tell me what tools it has available?
Listing the available World Bank MCP tools.
Request and response response (2.9 k chars)
{
"chart_types": [
{
"id": "line",
"description": "Line chart for temporal trends.",
"when_to_use": "Single indicator, 1+ countries, multiple years.",
"data_requirements": "time_period + obs_value. Color-codes countries automatically."
},
{
"id": "bar",
"description": "Horizontal bar chart for ranking/comparison.",
"when_to_use": "Single indicator, multiple countries, typically one year.",
"data_requirements": "obs_value + country dimension."
},
{
"id": "scatter",
"description": "Scatterplot for correlation between two indicators.",
"when_to_use": "Exactly 2 indicator_ids, multiple countries, single year.",
"data_requirements": "Requires indicator_ids list with 2 entries in get_multi_indicator_viz_spec."
},
{
"id": "connected_scatter",
"description": "Connected scatterplot: 2 indicators over time.",
"when_to_use": "Exactly 2 indicator_ids, multiple countries, multiple years.",
"data_requirements": "Same as scatter but multi-year."
},
{
"id": "layered_lines",
"description": "Dual/multi-axis line chart for 2-3 indicators in one country.",
"when_to_use": "2-3 indicator_ids, typically 1 country, multi-year.",
"data_requirements": "Requires indicator_ids list in get_multi_indicator_viz_spec."
},
{
"id": "small_multiples",
"description": "Faceted panel chart for breakdown × country comparisons.",
"when_to_use": "1 indicator, multiple breakdowns (sex/age) or many countries.",
"data_requirements": "Disaggregation dimensions with multiple values."
},
{
"id": "strip",
"description": "Strip/beeswarm chart for cross-country distribution.",
"when_to_use": ">8 countries, single year.",
"data_requirements": "obs_value + many country values."
},
{
"id": "area",
"description": "Stacked area chart for part-to-whole composition over time.",
"when_to_use": "User asks about composition, share, or breakdown over time.",
"data_requirements": "Requires multiple breakdown series or indicator_ids that sum to a whole."
},
{
"id": "heatmap",
"description": "Heatmap matrix for high-cardinality time series.",
"when_to_use": ">8 countries over multiple years without breakdowns.",
"data_requirements": "Automatically selected for dense country x year data."
},
{
"id": "map",
"description": "Geographic choropleth map.",
"when_to_use": "User asks for a map or spatial distribution across countries.",
"data_requirements": "Single indicator across multiple countries."
}
],
"multi_indicator_note": "For scatter, connected_scatter, and layered_lines, use data360_get_multi_indicator_viz_spec with an indicator_ids list."
}
It's a wrapper around the World Bank's Data360 API — 15 tools, and the design is quite deliberately staged: you're expected to discover IDs before you query, never guess them.
Discovery (start here)
- data360_search_datasets — find source databases (WDI, Findex, WGI, PIP…)
- data360_search_indicators — find indicators by topic; takes a single query, or a queries list for multiple topics, or query_groups when different topics need different countries
- data360_list_indicators — the full indicator list for one database
- data360_find_codelist_value — resolve names to dimension codes (country, sex, age, urbanisation)
- data360_expand_country_group — turn a group code like SAS or LIC into member countries
Inspection
- data360_get_metadata — methodology, source notes, limitations, coverage for a given country
- data360_get_disaggregation — available dimensions and years before you query
Retrieval and analysis
- data360_get_data — the actual observations, paginated
- data360_compare_countries — 2 to 8 countries, snapshot or time series
- data360_rank_countries — leaderboards by year, within a group or globally
- data360_summarize_data — summary statistics grouped by dimension
- data360_get_data_api_url — the raw API URL, for when you want the query itself rather than the result
Visualisation — these return Vega-Lite specs rather than images
- data360_get_viz_spec — one indicator
- data360_get_multi_indicator_viz_spec — 2–4 indicators
- data360_get_supported_chart_types — ten types: line, bar, scatter, connected_scatter, layered_lines, small_multiples, strip, area, heatmap, map
Two things stand out next to the other servers we've used. It's much more prescriptive about sequencing — several tool descriptions explicitly say the IDs must already be in context, which is a reasonable design response to models inventing plausible-looking indicator codes. And the Vega-Lite output makes it composable: you get a spec you could drop into a notebook or a web page rather than a picture.
What would you like to pull? A topic or a country and I'll work through the discovery steps.
Let's start by identifying indicators relevant to population so we can start to focus our questions.
Request and response request · response (6.0 k chars)
{
"limit": 15,
"query": "population"
}
{"count":15,"total_count":200,"offset":0,"has_more":true,"next_offset":15,"indicators":[{"idno":"WB_WDI_SP_POP_TOTL","database_id":"WB_WDI","database_name":"World Development Indicators (WDI)","name":"Population, total","truncated_definition":"Total population is based on the de facto definition of population, which counts all residents regar","periodicity":"Annual","latest_data":"2025","time_period_range":"1960-2025","covers_country":null,"requested_country":null,"dimensions":null,"primary_source_of":null},{"idno":"WB_WDI_SP_POP_TOTL_FE_IN","database_id":"WB_WDI","database_name":"World Development Indicators (WDI)","name":"Population, female","truncated_definition":"Female population is based on the de facto definition of population, which counts all female residen","periodicity":"Annual","latest_data":"2025","time_period_range":"1960-2025","covers_country":null,"requested_country":null,"dimensions":null,"primary_source_of":null},{"idno":"WB_WDI_SP_POP_TOTL_MA_IN","database_id":"WB_WDI","database_name":"World Development Indicators (WDI)","name":"Population, male","truncated_definition":"Male population is based on the de facto definition of population, which counts all male residents r","periodicity":"Annual","latest_data":"2025","time_period_range":"1960-2025","covers_country":null,"requested_country":null,"dimensions":null,"primary_source_of":null},{"idno":"WB_HNP_SP_POP_5Y","database_id":"WB_HNP","database_name":"Health Nutrition and Population Statistics","name":"Population, age group","truncated_definition":"The indicator refers to the number of males or females within a specific age group.","periodicity":"Annual","latest_data":"2023","time_period_range":"1960-2023","covers_country":null,"requested_country":null,"dimensions":["SEX","AGE"],"primary_source_of":null},{"idno":"WB_WDI_EN_URB_LCTY","database_id":"WB_WDI","database_name":"World Development Indicators (WDI)","name":"Population in largest city","truncated_definition":"Population in largest city is the urban population living in the country's largest metropolitan area","periodicity":"Annual","latest_data":"2024","time_period_range":"1960-2024","covers_country":null,"requested_country":null,"dimensions":null,"primary_source_of":null},{"idno":"WB_WDI_SP_URB_TOTL","database_id":"WB_WDI","database_name":"World Development Indicators (WDI)","name":"Urban population","truncated_definition":"Urban population refers to people living in urban areas as defined by national statistical offices. ","periodicity":"Annual","latest_data":"2025","time_period_range":"1960-2025","covers_country":null,"requested_country":null,"dimensions":null,"primary_source_of":null},{"idno":"WB_LPGD_SE_LPV_POP","database_id":"WB_LPGD","database_name":"Learning Poverty Global Database","name":"Population for Reference Year 2019","truncated_definition":"Population for Reference Year 2019","periodicity":"Annual","latest_data":"2023","time_period_range":"2001-2023","covers_country":null,"requested_country":null,"dimensions":["SEX","AGE"],"primary_source_of":null},{"idno":"WB_WDI_SP_RUR_TOTL","database_id":"WB_WDI","database_name":"World Development Indicators (WDI)","name":"Rural population","truncated_definition":"Rural population refers to people living in rural areas as defined by national statistical offices. ","periodicity":"Annual","latest_data":"2025","time_period_range":"1960-2025","covers_country":null,"requested_country":null,"dimensions":null,"primary_source_of":null},{"idno":"WB_WDI_EN_POP_DNST","database_id":"WB_WDI","database_name":"World Development Indicators (WDI)","name":"Population density (people per sq. km of land area)","truncated_definition":"Population density is midyear population divided by land area in square kilometers. Population is ba","periodicity":"Annual","latest_data":"2023","time_period_range":"1961-2023","covers_country":null,"requested_country":null,"dimensions":null,"primary_source_of":null},{"idno":"WB_WDI_EN_URB_MCTY","database_id":"WB_WDI","database_name":"World Development Indicators (WDI)","name":"Population in urban agglomerations of more than 1 million","truncated_definition":"Population in urban agglomerations of more than one million is the country's population living in me","periodicity":"Annual","latest_data":"2024","time_period_range":"1960-2024","covers_country":null,"requested_country":null,"dimensions":null,"primary_source_of":null},{"idno":"WB_WDI_SP_POP_GROW","database_id":"WB_WDI","database_name":"World Development Indicators (WDI)","name":"Population growth (annual %)","truncated_definition":"Annual population growth rate for year t is the exponential rate of growth of midyear population fro","periodicity":"Annual","latest_data":"2025","time_period_range":"1961-2025","covers_country":null,"requested_country":null,"dimensions":null,"primary_source_of":null},{"idno":"OECD_IDD_POP_METH2011","database_id":"OECD_IDD","database_name":"Income Distribution Database","name":"Number of individuals (Income definition until 2011)","truncated_definition":"Number of individuals (Income definition until 2011)","periodicity":"Annual","latest_data":"2013","time_period_range":"1976-2013","covers_country":null,"requested_country":null,"dimensions":["AGE"],"primary_source_of":null},{"idno":"WB_HNP_SH_HIV","database_id":"WB_HNP","database_name":"Health Nutrition and Population Statistics","name":"Population living with HIV","truncated_definition":"It is the percentage of people who are infected with HIV.","periodicity":"Annual","latest_data":"2023","time_period_range":"1960-2023","covers_country":null,"requested_country":null,"dimensions":["AGE"],"primary_source_of":null},{"idno":"WB_WDI_EN_URB_LCTY_UR_ZS","database_id":"WB_WDI","database_name":"World Development Indicators (WDI)","name":"Population in the largest city (% of urban population)","truncated_definition":"Population in largest city is the percentage of a country's urban population living in that country'","periodicity":"Annual","latest_data":"2025","time_period_ran
… [truncated, 6618 characters in total]
Identifying key indicators relevant to population dynamics.
Request and response request · response (8.1 k chars)
{
"limit": 3,
"queries": [
"age dependency ratio",
"fertility rate",
"net migration",
"life expectancy at birth"
],
"result_layout": "by_query"
}
{
"indicators": [],
"results": [
{
"query": "age dependency ratio",
"country_code": null,
"indicators": [
{
"idno": "WB_WDI_SP_POP_DPND",
"database_id": "WB_WDI",
"database_name": "World Development Indicators (WDI)",
"name": "Age dependency ratio (% of working-age population)",
"truncated_definition": "Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the wor",
"periodicity": "Annual",
"latest_data": "2025",
"time_period_range": "1960-2025",
"covers_country": null,
"requested_country": null,
"dimensions": null,
"primary_source_of": null
},
{
"idno": "WB_WDI_SP_POP_DPND_OL",
"database_id": "WB_WDI",
"database_name": "World Development Indicators (WDI)",
"name": "Age dependency ratio, old (% of working-age population)",
"truncated_definition": "Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-ag",
"periodicity": "Annual",
"latest_data": "2025",
"time_period_range": "1960-2025",
"covers_country": null,
"requested_country": null,
"dimensions": null,
"primary_source_of": null
},
{
"idno": "WB_WDI_SP_POP_DPND_YG",
"database_id": "WB_WDI",
"database_name": "World Development Indicators (WDI)",
"name": "Age dependency ratio, young (% of working-age population)",
"truncated_definition": "Age dependency ratio, young, is the ratio of younger dependents--people younger than 15--to the work",
"periodicity": "Annual",
"latest_data": "2025",
"time_period_range": "1960-2025",
"covers_country": null,
"requested_country": null,
"dimensions": null,
"primary_source_of": null
}
],
"count": 3,
"error": null
},
{
"query": "fertility rate",
"country_code": null,
"indicators": [
{
"idno": "WB_WDI_SP_DYN_WFRT",
"database_id": "WB_WDI",
"database_name": "World Development Indicators (WDI)",
"name": "Wanted fertility rate (births per woman)",
"truncated_definition": "Wanted fertility rate is an estimate of what the total fertility rate would be if all unwanted birth",
"periodicity": "Annual",
"latest_data": "2023",
"time_period_range": "1985-2023",
"covers_country": null,
"requested_country": null,
"dimensions": null,
"primary_source_of": null
},
{
"idno": "WB_WDI_SP_DYN_TFRT_IN",
"database_id": "WB_WDI",
"database_name": "World Development Indicators (WDI)",
"name": "Fertility rate, total (births per woman)",
"truncated_definition": "Total fertility rate represents the number of children that would be born to a woman if she were to ",
"periodicity": "Annual",
"latest_data": "2024",
"time_period_range": "1960-2024",
"covers_country": null,
"requested_country": null,
"dimensions": null,
"primary_source_of": null
},
{
"idno": "BS_SGI_81",
"database_id": "BS_SGI",
"database_name": "Sustainable Governance Indicators (SGI)",
"name": "Sustainable Governance Indicators: Fertility Rate",
"truncated_definition": "Please refer to: https://www.sgi-network.org/docs/2022/basics/SGI2022_Codebook.pdf",
"periodicity": "Annual",
"latest_data": "2022",
"time_period_range": "2014-2022",
"covers_country": null,
"requested_country": null,
"dimensions": null,
"primary_source_of": null
}
],
"count": 3,
"error": null
},
{
"query": "net migration",
"country_code": null,
"indicators": [
{
"idno": "WB_WDI_SM_POP_NETM",
"database_id": "WB_WDI",
"database_name": "World Development Indicators (WDI)",
"name": "Net migration",
"truncated_definition": "Net migration is the net total of migrants during the period, that is, the number of immigrants minu",
"periodicity": "Annual",
"latest_data": "2025",
"time_period_range": "1960-2025",
"covers_country": null,
"requested_country": null,
"dimensions": null,
"primary_source_of": null
},
{
"idno": "WB_WDI_SM_POP_TOTL",
"database_id": "WB_WDI",
"database_name": "World Development Indicators (WDI)",
"name": "International migrant stock, total",
"truncated_definition": "International migrant stock, total is the number of people at mid-year born in a country other than ",
"periodicity": "Annual",
"latest_data": "2024",
"time_period_range": "1990-2024",
"covers_country": null,
"requested_country": null,
"dimensions": null,
"primary_source_of": null
},
{
"idno": "WB_KNOMAD_MIG",
"database_id": "WB_KNOMAD",
"database_name": "The Global Knowledge Partnership on Migration and Development (KNOMAD) database",
"name": "Bilateral Estimate of Migrant Stocks",
"truncated_definition": "Estimate of Migrant Stocks",
"periodicity": "Annual",
"latest_data": "2021",
"time_period_range": "2021-2021",
"covers_country": null,
"requested_country": null,
"dimensions": null,
"primary_source_of": null
}
],
"count": 3,
"error": null
},
{
"query": "life expectancy at birth",
"country_code": n
… [202 characters omitted from the middle; 8202 in total] …
e expectancy at birth, total (years)",
"truncated_definition": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing pat",
"periodicity": "Annual",
"latest_data": "2024",
"time_period_range": "1960-2024",
"covers_country": null,
"requested_country": null,
"dimensions": null,
"primary_source_of": null
},
{
"idno": "WB_WDI_SP_DYN_LE00_MA_IN",
"database_id": "WB_WDI",
"database_name": "World Development Indicators (WDI)",
"name": "Life expectancy at birth, male (years)",
"truncated_definition": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing pat",
"periodicity": "Annual",
"latest_data": "2024",
"time_period_range": "1960-2024",
"covers_country": null,
"requested_country": null,
"dimensions": null,
"primary_source_of": null
},
{
"idno": "WB_WDI_SP_DYN_LE00_FE_IN",
"database_id": "WB_WDI",
"database_name": "World Development Indicators (WDI)",
"name": "Life expectancy at birth, female (years)",
"truncated_definition": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing pat",
"periodicity": "Annual",
"latest_data": "2024",
"time_period_range": "1960-2024",
"covers_country": null,
"requested_country": null,
"dimensions": null,
"primary_source_of": null
}
],
"count": 3,
"error": null
}
],
"result_layout": "by_query",
"queries": [
"age dependency ratio",
"fertility rate",
"net migration",
"life expectancy at birth"
],
"required_country": null,
"country_names": null,
"total_candidates": 12,
"deduplicated_count": 0,
"error": null
}
Searching on "population" alone returns 200 indicators, so here's the shape of what's there, plus the adjacent demographic measures that usually matter more for framing a question.
Stocks — how many people
| Indicator | ID | Coverage |
|---|---|---|
| Population, total | WB_WDI_SP_POP_TOTL |
1960–2025 |
| Population, female / male | WB_WDI_SP_POP_TOTL_FE_IN / ..._MA_IN |
1960–2025 |
| Population, age group | WB_HNP_SP_POP_5Y |
1960–2023, dims: SEX, AGE |
That last one is the only genuine age-structure series here — it carries SEX and AGE dimensions, so it's what you'd use for pyramids or cohort work. Note it's in a different database (WB_HNP, Health Nutrition and Population) and stops two years earlier than the WDI headline series.
Rates and structure
- Population growth, annual % — WB_WDI_SP_POP_GROW (1961–2025)
- Age dependency ratio, total / old / young — WB_WDI_SP_POP_DPND, _OL, _YG (1960–2025)
- Fertility rate, total — WB_WDI_SP_DYN_TFRT_IN (1960–2024)
- Life expectancy at birth, total / male / female — WB_WDI_SP_DYN_LE00_IN, _MA_IN, _FE_IN (1960–2024)
Distribution — where people are
- Urban / rural population — WB_WDI_SP_URB_TOTL, WB_WDI_SP_RUR_TOTL
- Population density — WB_WDI_EN_POP_DNST (1961–2023)
- Population in largest city — WB_WDI_EN_URB_LCTY, and as a share of urban population WB_WDI_EN_URB_LCTY_UR_ZS
- Population in agglomerations over 1 million — WB_WDI_EN_URB_MCTY
Movement
- Net migration — WB_WDI_SM_POP_NETM (1960–2025)
- International migrant stock — WB_WDI_SM_POP_TOTL (1990–2024)
One practical caution before you pick: end years vary (2023, 2024, 2025) and the 2025 values in WDI are projections rather than observations. If you're going to combine series — say density against dependency ratio — the overlap window is what constrains you, not the longest individual series. data360_get_metadata will give the methodology and source notes for whichever you settle on.
What's the question you want to ask of these?
For total population and net migration, can you compare Australia, New Zealand and Canada?
Request and response request · response (7.4 k chars)
{
"country_codes": "AUS;NZL;CAN",
"database_id": "WB_WDI",
"end_year": 2024,
"include_time_series": true,
"indicator_id": "WB_WDI_SP_POP_TOTL",
"start_year": 2000
}
{
"indicator": "Population, total",
"unit": "Persons",
"snapshot": {
"year": "2024",
"year_selection_note": "Latest year with data for all compared countries: 2024 (3/3 countries)",
"rankings": [
{
"rank": 1,
"code": "CAN",
"country": "Canada",
"value": 41262329.0,
"claim_id": "1cd82a87"
},
{
"rank": 2,
"code": "AUS",
"country": "Australia",
"value": 27194286.0,
"claim_id": "14a7c443"
},
{
"rank": 3,
"code": "NZL",
"country": "New Zealand",
"value": 5290000.0,
"claim_id": "fdbeace0"
}
],
"spread": {
"min": 5290000.0,
"max": 41262329.0,
"range": 35972329.0,
"coefficient_of_variation": 0.7374
}
},
"time_series": {
"year_range": "2000-2024",
"n_aligned_years": 25,
"convergence": "parallel",
"cagr": {
"AUS": 1.5,
"NZL": 1.32,
"CAN": 1.24
},
"series_schema": [
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],
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"2018",
37072620.0,
"03c9dd9c"
],
[
"2019",
37618495.0,
"0efb7523"
],
[
"2020",
38028638.0,
"33c11b97"
],
[
"2021",
38239864.0,
"caf7f07a"
],
[
"2022",
38950132.0,
"e2fd4bc0"
],
[
"2023",
40049088.0,
"9fc3caf0"
],
[
"2024",
41262329.0,
"1cd82a87"
]
]
}
},
"country_names": {
"AUS": "Australia",
"NZL": "New Zealand",
"CAN": "Canada"
},
"error": null
}
Charting population and migration trends over time.
Total population, 2000\u20132024 (millions)
All three grew steadily, but the compound annual growth rates invert the intuition you'd get from the absolute numbers: Australia 1.50%, New Zealand 1.32%, Canada 1.24%. Canada added the most people (10.6 million) while growing proportionally the slowest of the three over the full period. What the chart does show clearly is Canada's curve bending upward after 2021 — a genuine break in trend, not a continuation.
Net migration explains most of it:
Net migration, 2000\u20132024 (persons)
The three trajectories diverge sharply after 2020:
Canada steps up twice — once around 2016 (from ~220k to ~400k) and again after the pandemic dip, peaking at 459,988 in 2022. It has not returned to its pre-2016 baseline.
Australia peaked earlier, at 275,773 in 2008, then collapsed to 44,447 in 2020 under closed borders. The recovery has settled around 140,000 — roughly half its 2016–17 level. This is the one that most contradicts the public narrative of a post-pandemic migration surge; on this series Australia's intake is well below its own 2010s norm.
New Zealand is volatile at a different order of magnitude, swinging negative in four of the 25 years (2000, 2011, 2012, 2021). Peak was 75,512 in 2016.
As a share of 2024 population, the ordering is Canada 0.89%, Australia 0.51%, New Zealand 0.35%.
Three caveats from the data itself rather than the interpretation:
The tool returned a null CAGR for New Zealand's migration series — compound growth is undefined across sign changes. That's correct behaviour, but it means the CAGR field silently can't be used for any flow measure that can go negative.
New Zealand's 2022 and 2023 values are identical to the person (21,404), which almost certainly means one year was carried forward rather than observed. And its 2023–24 population figures are suspiciously round (5,200,000 and 5,290,000) where Australia's and Canada's are precise to the digit — different estimation methods behind the same series.
Worth running data360_get_metadata on the migration indicator before you lean on it hard; net migration in WDI is typically derived as a residual from census and vital-statistics data, not measured directly, which is why it's revised heavily and why five-year interpolation artefacts show up.
Mapbox
Can you use the Mapbox MCP to show me locations within 10, 20 and 30 minutes walk of the State Library in Melbourne?
Locating coordinates for a library building.
Finding a workaround for a missing geocoding tool.
Request and response request · response (0.8 k chars)
{
"country": [
"AU"
],
"proximity": {
"latitude": -37.8136,
"longitude": 144.9631
},
"q": "State Library Victoria",
"types": [
"poi"
]
}
1. State Library Victoria
Address: Melbourne, Melbourne 3000, Australia
Coordinates: -37.80969865708734, 144.96548809111118
Type: poi
Category: tourist attraction
Mapbox ID: dXJuOm1ieHBvaToyMjI0MzUzZC1mYTU3LTRjM2QtOTY2Yi03OThkMWViZjVjMDU
2. State Library Victoria
Address: Bell Ave, Ballarat 3350, Australia
Coordinates: -37.62290336, 143.89752559
Type: poi
Category: services, self storage
Mapbox ID: dXJuOm1ieHBvaTo4NzEzODRjZC0wNmVlLTQ2MjgtYjM0Ni0xNDQ4YTdlMDE0MTI
📍 To show this on a live Mapbox GL JS map, call:
render_map_tool({ "payload_refs": ["mapbox://selffetch/search?data=eyJxIjoiU3RhdGUgTGlicmFyeSBWaWN0b3JpYSIsInByb3hpbWl0eSI6eyJsb25naXR1ZGUiOjE0NC45NjMxLCJsYXRpdHVkZSI6LTM3LjgxMzZ9LCJjb3VudHJ5IjpbIkFVIl0sInR5cGVzIjpbInBvaSJdfQ"] })
Request and response request · response (0.7 k chars)
{
"contours_colors": [
"2563eb",
"7c3aed",
"b45309"
],
"contours_minutes": [
10,
20,
30
],
"coordinates": {
"latitude": -37.80969865708734,
"longitude": 144.96548809111118
},
"generalize": 100,
"polygons": true,
"profile": "mapbox/walking"
}
Found 3 isochrone contours:
1. Polygon contour for 30 minutes travel time
Color: #b45309
Fill: #b45309 (opacity: 0.33)
2. Polygon contour for 20 minutes travel time
Color: #7c3aed
Fill: #7c3aed (opacity: 0.33)
3. Polygon contour for 10 minutes travel time
Color: #2563eb
Fill: #2563eb (opacity: 0.33)
📍 To show this on a live Mapbox GL JS map, call:
render_map_tool({ "payload_refs": ["mapbox://selffetch/isochrone?data=eyJwcm9maWxlIjoibWFwYm94L3dhbGtpbmciLCJjb29yZGluYXRlcyI6eyJsb25naXR1ZGUiOjE0NC45NjU0ODgwOTExMTExOCwibGF0aXR1ZGUiOi0zNy44MDk2OTg2NTcwODczNH0sImNvbnRvdXJzX21pbnV0ZXMiOlsxMCwyMCwzMF0sImNvbnRvdXJzX2NvbG9ycyI6WyIyNTYzZWIiLCI3YzNhZWQiLCJiNDUzMDkiXSwicG9seWdvbnMiOnRydWUsImdlbmVyYWxpemUiOjEwMH0"] })
Centred on the Swanston Street entrance (-37.8097, 144.9655). Blue is 10 minutes, purple 20, orange 30 — each contour is the full reachable area, so the bands nest rather than sitting side by side.
Note the shape is lopsided rather than circular, which is the point of using isochrones over a radius buffer. The contours stretch further north and east along the street grid and pull in sharply where the Yarra and the rail corridors cut the pedestrian network — you can't walk across water or through Southern Cross's tracks, so those directions cost more minutes per metre of straight-line distance.
One caveat on the geocode: the search returned two "State Library Victoria" results, the second being a self-storage business in Ballarat. I took the Melbourne tourist-attraction match, but it's a reminder that POI geocoding gives you plausible wrong answers as readily as right ones.
If you'd like POIs rather than just the areas, ground_location_tool returns nearby places filtered by category alongside the reachability contours — tell me what kind of thing you're looking for and I'll pull it.
What sort of locations within the CBD can the server help us to locate?
Request and response request · response (6.0 k chars)
{
"uri": "mapbox://categories"
}
{
"listItems": [
"shopping",
"services",
"food_and_drink",
"food",
"office",
"restaurant",
"health_services",
"education",
"apartment_or_condo",
"grocery",
"transportation",
"place_of_worship",
"clothing_store",
"outdoors",
"salon",
"lodging",
"supermarket",
"wholesale_store",
"financial_services",
"auto_repair",
"nongovernmental_organization",
"school",
"beauty_store",
"government",
"cafe",
"bus_stop",
"fast_food",
"hairdresser",
"park",
"real_estate_agent",
"pharmacy",
"nightlife",
"sports",
"hotel",
"bank",
"temple",
"tourist_attraction",
"medical_practice",
"bar",
"chinese_restaurant",
"church",
"doctors_office",
"coffee_shop",
"coffee",
"bakery",
"farm",
"medical_clinic",
"home",
"hospital",
"fitness_center",
"commercial",
"factory",
"furniture_store",
"entertainment",
"convenience_store",
"car_dealership",
"dentist",
"repair_shop",
"consulting",
"electronics_shop",
"parking_lot",
"lawyer",
"hospital_unit",
"shipping_store",
"shopping_mall",
"photographer",
"mosque",
"clothing",
"elementary_school",
"gas_station",
"atm",
"market",
"hardware_store",
"travel_agency",
"historic_site",
"phone_store",
"advertising_agency",
"dessert_shop",
"alternative_healthcare",
"insurance_broker",
"post_office",
"kindergarten",
"shoe_store",
"monument",
"jewelry_store",
"it",
"warehouse",
"mountain",
"river",
"laundry",
"playground",
"car_wash",
"gift_shop",
"psychotherapist",
"lake",
"pet_store",
"spa",
"ice_cream",
"sports_club",
"japanese_restaurant",
"massage_shop",
"florist",
"garden",
"butcher_shop",
"bed_and_breakfast",
"tailor",
"physiotherapist",
"teahouse",
"womens_clothing_store",
"field",
"care_services",
"charging_station",
"cemetery",
"car_rental",
"paper_goods_store",
"book_store",
"photo_store",
"psychological_services",
"pizza_restaurant",
"internet_cafe",
"college",
"nail_salon",
"community_center",
"copyshop",
"landscaping",
"korean_restaurant",
"chiropractor",
"tobacco_shop",
"liquor_store",
"hostel",
"event_planner",
"asian_restaurant",
"veterinarian",
"design_studio",
"childcare",
"employment_agency",
"public_transportation_station",
"social_club",
"university",
"event_space",
"home_repair",
"optician",
"assisted_living_facility",
"medical_supply_store",
"studio",
"bridge",
"taxi",
"museum",
"art",
"sports_shop",
"bicycle_shop",
"buddhist_temple",
"snack_bar",
"health_food_store",
"high_school",
"motorcycle_dealer",
"equipment_rental",
"police_station",
"arts_and_craft_store",
"tutor",
"medical_laboratory",
"driving_school",
"tattoo_parlour",
"food_court",
"deli",
"boutique",
"mexican_restaurant",
"nightclub",
"library",
"indian_restaurant",
"tax_advisor",
"fashion_accessory_shop",
"fabric_store",
"burger_restaurant",
"dance_studio",
"language_school",
"forest",
"dry_cleaners",
"department_store",
"island",
"barbeque_restaurant",
"storage",
"news_kiosk",
"mattress_store",
"counselling",
"seafood_restaurant",
"soccer_field",
"funeral_home",
"video_game_store",
"breakfast_restaurant",
"diner_restaurant",
"noodle_restaurant",
"music_school",
"fire_station",
"brunch_restaurant",
"art_gallery",
"juice_bar",
"toy_store",
"baby_goods_shop",
"karaoke_bar",
"yoga_studio",
"government_offices",
"recording_studio",
"fishing_store",
"bus_station",
"italian_restaurant",
"indonesian_restaurant",
"conference_center",
"swimming_pool",
"furniture_maker",
"townhall",
"theme_park",
"lighting_store",
"pub",
"theatre",
"notary",
"sushi_restaurant",
"university_building",
"stadium",
"recycling_center",
"charity",
"winery",
"laboratory",
"dormitory",
"music_shop",
"motel",
"antique_shop",
"cinema",
"golf_course",
"martial_arts_studio",
"music_venue",
"public_artwork",
"outdoor_sculpture",
"nature_reserve",
"american_restaurant",
"railway_station",
"pawnshop",
"garden_store",
"discount_store",
"waste_transfer_station",
"watch_store",
"sandwich_shop",
"lounge",
"taco_shop",
"rehabilitation_center",
"labor_union",
"party_store",
"tourist_information",
"tennis_courts",
"rest_area",
"radio_studio",
"thrift_shop",
"pilates_studio",
"trade_school",
"buffet_restaurant",
"vape_shop",
"locksmith",
"bridal_shop",
"thai_restaurant",
"canal",
"leather_goods",
"casino",
"psychic",
"video_store",
"coworking_space",
"recreation_center",
"kitchen_store",
"courthouse",
"boat_rental",
"campground",
"brewery",
"basketball_court",
"vacation_rental",
"cobbler",
"cocktail_bar",
"outlet_store",
"sewing_shop",
"shoe_repair",
"arts_center",
"bubble_tea",
"billiards",
"gymnastics",
"ramen_restaurant",
"fair_grounds",
"concert_hall",
"beach",
"herbalist",
"french_restaurant",
"hobby_shop",
"donut_shop",
"steakhouse",
"tanning_salon",
"camera_shop",
"currency_exchange",
"viewpoint",
"cannabis_dispensary",
"television_studio",
"frame_store",
"animal_shelter",
"stable",
"carpet_store",
"tours",
"wine_bar",
"baseball_field",
"optometri
… [truncated, 9834 characters in total]
Organizing categories into a grouped overview with examples.
The server exposes a fixed taxonomy of several hundred canonical categories. Two tools consume it: category_search_tool for generic types ("all the museums"), and search_and_geocode_tool for named places or brands. Both accept a bbox, so you can hard-constrain results to the Hoddle Grid rather than just biasing toward it.
For a CBD, the useful clusters are:
Culture and civic — museum, art_gallery, library, theatre, concert_hall, arts_center, cinema, music_venue, historic_site, monument, public_artwork, outdoor_sculpture, exhibit, courthouse, townhall, government_offices, police_station, post_office, embassy
Transport — railway_station, light_rail_station, bus_stop, bus_station, public_transportation_station, taxi, bike_rental, parking_lot, charging_station, boat_or_ferry, pier
Education and work — university, university_building, college, library, language_school, music_school, coworking_space, conference_center, meeting_room, event_space, laboratory
Food and drink — a very fine-grained branch: cafe, coffee_shop, coffee_roaster, bakery, bar, pub, wine_bar, cocktail_bar, brewery, bubble_tea, teahouse, plus about forty cuisine-specific restaurant types (vietnamese_restaurant, ramen_restaurant, greek_restaurant, fish_and_chips_restaurant…)
Retail and services — book_store, music_shop, department_store, shopping_mall, market, pharmacy, bank, atm, currency_exchange, laundry, hairdresser
Green and open space — park, garden, plaza, playground, fountain, viewpoint, river, bridge
Accommodation — hotel, hostel, motel, bed_and_breakfast, vacation_rental, resort
Two limits worth flagging before you build anything on it. The taxonomy is closed — category_search_tool only accepts canonical names, so "heritage arcade" or "laneway bar" has to be mapped onto something in the list. And limit caps at 25 results per call, which for a dense CBD means you're sampling rather than enumerating; exhaustive coverage needs tiled bounding boxes.
Name a category or two and I'll run them within the CBD bounds and put the results on a map.
OK. Let's see on a map every University within a 30 minute commute of the State library.
Figuring out the right travel mode for a commute map.
Before I run it — "commute" changes the answer a lot here, and Mapbox's routing profiles don't include public transport, so the options are walking, cycling, or driving. A 30-minute walk keeps you inside the inner city; a 30-minute drive reaches the outer-suburban campuses.
Which travel mode should the 30-minute isochrone use?
Driving (with traffic)
That's the 25-result cap biting exactly as flagged — proximity bias filled every slot with RMIT buildings and CBD private colleges within 1 km, and never reached the suburban campuses. Let me search by sector instead:
The category search returns building-level POIs rather than institutions — at Bundoora it gave me ten La Trobe lecture theatres. For "every university" I need named-campus geocoding instead:
Search and Geocode Tool
Compiling a list of university campus locations.
Request and response request · response (2.4 k chars)
{
"country": [
"AU"
],
"proximity": {
"latitude": -37.82,
"longitude": 145.02
},
"q": "Swinburne University of Technology Hawthorn campus",
"types": [
"poi"
]
}
1. Swinburne University of Technology (Swinburne University of Technology)
Address: John St, Melbourne 3122, Australia
Coordinates: -37.82262106, 145.03811275
Type: poi
Category: education, university
Mapbox ID: dXJuOm1ieHBvaTpiYjg2NTU5ZC02YWM5LTQ0OWItYjA4YS1lOTE5MDk4MjQyZDY
2. Swinburne University of Technology Hawthorn Library (Swinburne University of Technology Hawthorn Library)
Address: 418 Burwood Rd, Melbourne 3122, Australia
Coordinates: -37.8225358, 145.03924413
Type: poi
Category: education, library
Mapbox ID: dXJuOm1ieHBvaTpiNGU4MzQyMi1mMjZjLTRiYTYtYTM4Yy00MWI3YmJkODRhMmM
3. Swinburne University of Technology (Swinburne University of Technology)
Address: John St, Melbourne 3122, Australia
Coordinates: -37.82240584, 145.03910488
Type: poi
Category:
Mapbox ID: dXJuOm1ieHBvaTplZDQwMThmMi0zMWJkLTRhOGUtODQ0OC02MDE2YjQ2ZjljMDg
4. Swinburne University of Technology (Swinburne University of Technology)
Address: 369 Stud Rd, Melbourne 3152, Australia
Coordinates: -37.87348538, 145.23489317
Type: poi
Category: education, university
Mapbox ID: dXJuOm1ieHBvaTpkM2QxMjgzMy1iNjA4LTRkYTEtYmQzYy0yZjY3MTBhMjY2ZTQ
5. Swinburne University of Technology (Swinburne University of Technology)
Address: 12-50 Norton Rd, Melbourne 3136, Australia
Coordinates: -37.80164137, 145.28527792
Type: poi
Category: education, university
Mapbox ID: dXJuOm1ieHBvaTo3MTY2NGRjMC04NTVmLTQ1ZTMtODNhOS1hZGUxMWRmNmExZGM
6. Swinburne University of Technology Residential College (Swinburne University of Technology Residential College)
Address: Multi-Deck Car Park, Melbourne 3122, Australia
Coordinates: -37.82054568, 145.03751101
Type: poi
Category: college, education
Mapbox ID: dXJuOm1ieHBvaTo5MzlhNGU3ZC03YjgwLTQ1MTYtYWZjOC0yYzkyMjU5ZGFkZjQ
7. CSIRO - Swinburne University of Technology (CSIRO - Swinburne University of Technology)
Address: Gardiner Rd, Melbourne 3168, Australia
Coordinates: -37.90640522, 145.12810173
Type: poi
Category: education
Mapbox ID: dXJuOm1ieHBvaTpkNWY5MjcyYi0wZWQ2LTQ2MzEtOTEyOC0yODZkOGU5ZmMyMDI
📍 To show this on a live Mapbox GL JS map, call:
render_map_tool({ "payload_refs": ["mapbox://selffetch/search?data=eyJxIjoiU3dpbmJ1cm5lIFVuaXZlcnNpdHkgb2YgVGVjaG5vbG9neSBIYXd0aG9ybiBjYW1wdXMiLCJwcm94aW1pdHkiOnsibG9uZ2l0dWRlIjoxNDUuMDIsImxhdGl0dWRlIjotMzcuODJ9LCJjb3VudHJ5IjpbIkFVIl0sInR5cGVzIjpbInBvaSJdfQ"] })
Search and Geocode Tool
Calculating travel times from the library to each campus.
Splitting the distance request into two batched calls.
Request and response request · response (1.1 k chars)
{
"annotations": "duration,distance",
"coordinates": [
{
"latitude": -37.80969865708734,
"longitude": 144.96548809111118
},
{
"latitude": -37.72009161,
"longitude": 145.04926
},
{
"latitude": -37.67987835,
"longitude": 145.06335467
},
{
"latitude": -37.91097158,
"longitude": 145.1367085
},
{
"latitude": -37.87348538,
"longitude": 145.23489317
},
{
"latitude": -37.80164137,
"longitude": 145.28527792
}
],
"destinations": "1;2;3;4;5",
"profile": "mapbox/driving-traffic",
"sources": "0"
}
{
"code": "Ok",
"durations": [
[
2023,
2012.8,
1406.4,
1931.8,
2523.6
]
],
"distances": [
[
16418.5,
36129.6,
23227.9,
38395.8,
50214.1
]
],
"sources": [
{
"name": "Little Lonsdale Street",
"location": [
144.965702,
-37.810156
],
"distance": 54.10905702
}
],
"destinations": [
{
"name": "",
"location": [
145.049717,
-37.720228
],
"distance": 43.024602874
},
{
"name": "",
"location": [
145.062691,
-37.679211
],
"distance": 94.402005361
},
{
"name": "Scenic Boulevard",
"location": [
145.136581,
-37.910955
],
"distance": 11.41135546
},
{
"name": "",
"location": [
145.234846,
-37.873081
],
"distance": 45.030919516
},
{
"name": "",
"location": [
145.285119,
-37.801409
],
"distance": 29.309080523
}
]
}
Converting travel times into minutes for each location.
Ten campuses fall inside the 30-minute drive (green), four outside (grey). Times are point-to-point via matrix_tool on the driving-traffic profile, computed just now — Sunday afternoon, so these are close to best case.
| Campus | Time | Distance |
|---|---|---|
| RMIT City | 3 min | 0.6 km |
| ACU Melbourne (Fitzroy) | 4 min | 1.4 km |
| Monash Parkville | 9 min | 3.5 km |
| University of Melbourne, Parkville | 10 min | 1.9 km |
| University of Melbourne, Burnley | 13 min | 7.2 km |
| Swinburne Hawthorn | 15 min | 8.8 km |
| Victoria University, Footscray Park | 18 min | 8.3 km |
| Monash Caulfield | 19 min | 13.9 km |
| Monash Clayton | 23 min | 23.2 km |
| Deakin Burwood | 25 min | 16.1 km |
| Swinburne Wantirna | 32 min | 38.4 km |
| La Trobe Bundoora | 34 min | 16.4 km |
| RMIT Bundoora | 34 min | 36.1 km |
| Swinburne Croydon | 42 min | 50.2 km |
The freeway effect is stark. Monash Clayton is 23 km away but reachable in 23 minutes via the Monash Freeway; La Trobe Bundoora is 16 km — the third-closest campus by straight-line distance — and takes 34 minutes on arterial roads. Distance and time rank the list quite differently.
Two things to hold lightly. The ten campuses came from named-campus geocoding after category_search_tool failed twice: on the first attempt the 25-result cap filled entirely with RMIT buildings within 1 km of the library, and on the second it returned ten individual La Trobe lecture theatres rather than the institution. So this list depends on me having named the right campuses — if a private provider or a small satellite campus isn't in my list, it isn't on the map. The taxonomy indexes buildings, not institutions, which is a real limitation for this kind of question.
And "30 minutes" is doing two different jobs here: the orange contour is the isochrone polygon, while the pin classifications come from separate point-to-point routing. They mostly agree, but Swinburne Wantirna at 32 minutes sits close enough to the boundary that the generalised polygon and the exact route may disagree visually.
Wikidata
Let's chance to another topic. Can you use Wikidata to identify the elements in the periodic table, and plot out the table with DrawIO?
Querying chemical element data from Wikidata.
Request and response request · response (3.6 k chars)
{
"query": "chemical element"
}
Q11344: chemical element — species of atoms having the same number of protons in the atomic nucleus and the same chemical properties, but not necessarily the same mass, or the same stability (or half-lifetime if they are unstable) Q925: mercury — chemical element with symbol Hg and atomic number 80 Q56009873: elemenes — group of chemical compounds Q708: lead — chemical element with symbol Pb and atomic number 82 Q623: carbon — chemical element with symbol C and atomic number 6; common element of all known life Q1846: holmium — chemical element with symbol Ho and atomic number 67 Q743: tungsten — chemical element with symbol W and atomic number 74 Q1108: caesium — chemical element with symbol Cs and atomic number 55 Q618: boron — chemical element with symbol B and atomic number 5 Q1105: neptunium — chemical element with symbol Np and atomic number 93 Q941: yttrium — chemical element with symbol Y and atomic number 39 Q876: selenium — chemical element with symbol Se and atomic number 34 Q15627952: diethyl cadmium — chemical compound Q1086: ruthenium — chemical element with symbol Ru and atomic number 44 Q1099: antimony — chemical element with symbol Sb and atomic number of 51 Q1388: neodymium — chemical element with symbol Nd and atomic number 60 Q1849: erbium — chemical element with symbol Er and atomic number 68 Q428867: abundance of the chemical elements — any of several measures of the occurrence of different chemical elements Q19605: halogens — group of chemical elements in column 17 of the periodic table Q2207705: selenium tetrachloride — chemical compound Q1054: technetium — chemical element with symbol Tc and atomic number 43 Q722: vanadium — chemical element with symbol V and atomic number 23 Q706: calcium — chemical element with symbol Ca and atomic number 20 Q671: francium — chemical element with symbol Fr and atomic number 87 Q1087: rhodium — chemical element with symbol Rh and atomic number 45 Q879: bromine — chemical element with symbol Br and atomic number 35 Q15628212: europium selenide — chemical compound Q1843: dysprosium — chemical element with symbol Dy and atomic number 66 Q4651327: AEM — chemical compound Q1898: mendelevium — chemical element with symbol Md and atomic number 101 Q1046: niobium — chemical element with symbol Nb and atomic number 41 Q1090: silver — chemical element with symbol Ag and atomic number 47 Q556: hydrogen — chemical element with symbol H and atomic number 1 Q19862087: iodine (native) — vapour of natural origin (as opposed to the chemical element) Q420704: cadmium selenide — chemical compound Q130336: phenol — chemical compound C6H5OH Q1103: iodine — chemical element with symbol I and atomic number 53 Q3596763: o-phenylenediamine — chemical compound Q1014287: cadmium arsenide — chemical compound Q19577: actinide — f-block chemical elements Q1832: gadolinium — chemical element with symbol H and atomic number 64 Q1801: lanthanum — chemical element with symbol La and atomic number 57 Q252344: methyl diethanolamine — chemical compound Q1115: thorium — chemical element with symbol Th and atomic number 90 Q1872: americium — chemical element with symbol Am and atomic number 95 Q897: gold — chemical element with symbol Au and atomic number 79; a dense, malleable, and corrosion-resistant precious metal widely used as currency, in jewelry, electronics, and as a financial reserve asset Q6102450: list of chemical elements — Wikimedia list article Q1053: molybdenum — chemical element with symbol Mo and atomic number 42 Q727721: chemise — architecture Q1094: indium — chemical element with symbol In and atomic number 49
Request and response request · response (5.0 k chars)
{
"entity_id": "Q556",
"include_external_ids": false
}
hydrogen (Q556): description: chemical element with symbol H and atomic number 1 hydrogen (Q556): aliases: ₁H, element 1 hydrogen (Q556): oxidation state (P1121): -1 hydrogen (Q556): oxidation state (P1121): +1 hydrogen (Q556): oxidation state (P1121): +0 hydrogen (Q556): atomic number (P1086): +1 hydrogen (Q556): Commons category (P373): Hydrogen hydrogen (Q556): element symbol (P246): H hydrogen (Q556): topic's main category (P910): Category:Hydrogen (Q7215740) hydrogen (Q556): electronegativity (P1108): +2.2 hydrogen (Q556): instance of (P31): chemical element (Q11344) hydrogen (Q556): instance of (P31): atmophile element (Q2313842) hydrogen (Q556): image (P18): Hydrogen discharge tube.jpg hydrogen (Q556): discoverer or inventor (P61): Henry Cavendish (Q131733) hydrogen (Q556): time of discovery or invention (P575): 1766 hydrogen (Q556): Commons gallery (P935): Hydrogen hydrogen (Q556): described by source (P1343): Armenian Soviet Encyclopedia (Q2657718) hydrogen (Q556): described by source (P1343): Hydrogen (Q108309723) hydrogen (Q556): described by source (P1343): Encyclopædia Britannica 11th edition (Q867541) hydrogen (Q556): described by source (P1343): Meyers Konversations-Lexikon, 4th edition (1885–1890) (Q19219752) hydrogen (Q556): described by source (P1343): Brockhaus and Efron Encyclopedic Dictionary (Q602358) hydrogen (Q556): described by source (P1343): Small Brockhaus and Efron Encyclopedic Dictionary (Q19180675) hydrogen (Q556): described by source (P1343): Sytin Military Encyclopedia (Q4114391) hydrogen (Q556): different from (P1889): dihydrogen (Q3027893) hydrogen (Q556): different from (P1889): hydrogen atom (Q6643508) hydrogen (Q556): different from (P1889): hydrogen molecule (Q19822725) hydrogen (Q556): location of discovery (P189): United Kingdom (Q145) hydrogen (Q556): subclass of (P279): diatomic nonmetal (Q19753344) hydrogen (Q556): subclass of (P279): s-block (Q208106) hydrogen (Q556): subclass of (P279): reducing agent (Q216830) hydrogen (Q556): mass (P2067): +1.008 dalton hydrogen (Q556): on focus list of Wikimedia project (P5008): Wikipedia:List of articles all languages should have (Q5460604) hydrogen (Q556): on focus list of Wikimedia project (P5008): Wikipedia:Vital articles/Level/4 (Q6173448) hydrogen (Q556): on focus list of Wikimedia project (P5008): WikiProject Climate change (Q15305047) hydrogen (Q556): color (P462): colorless (Q1396399) hydrogen (Q556): permanent duplicated item (P2959): <no label> (Q57873859) hydrogen (Q556): part of (P361): period 1 (Q191936) hydrogen (Q556): part of (P361): group 1 (Q10801007) hydrogen (Q556): part of (P361): water (Q283) hydrogen (Q556): part of (P361): nonmetal (Q19600) hydrogen (Q556): refractive index (P1109): +1.000132 hydrogen (Q556): density (P2054): +0.089870 kilogram per cubic metre hydrogen (Q556): speed of sound (P2075): +1270 metre per second hydrogen (Q556): thermal conductivity (P2068): +0.1805 watt per metre kelvin hydrogen (Q556): ionization energy (P2260): +13.598 electronvolt hydrogen (Q556): melting point (P2101): -259.14 degree Celsius hydrogen (Q556): boiling point (P2102): -252.87 degree Celsius hydrogen (Q556): molar enthalpy of vaporization (P2116): +0.452 kilojoule per mole hydrogen (Q556): half-life (P2114): None hydrogen (Q556): named after (P138): water (Q283) hydrogen (Q556): electron configuration (P8000): 1s¹ hydrogen (Q556): has use (P366): hydrogen technologies (Q1854358) hydrogen (Q556): has use (P366): hydrogen fuel (Q11549077) hydrogen (Q556): video (P10): Hydrogen balloon explosion 2.webm hydrogen (Q556): ionic radius (P10685): +54 picometre hydrogen (Q556): Unicode character (P487): 氫 hydrogen (Q556): pronunciation audio (P443): Hydrogen-ar.ogg hydrogen (Q556): Stack Exchange tag (P1482): https://chemistry.stackexchange.com/tags/hydrogen hydrogen (Q556): Stack Exchange tag (P1482): https://physics.stackexchange.com/tags/hydrogen hydrogen (Q556): maintained by WikiProject (P6104): WikiProject Elements (Q11165030) hydrogen (Q556): maintained by WikiProject (P6104): WikiProject Astronomy (Q8493596) hydrogen (Q556): maintained by WikiProject (P6104): WikiProject Climate change (Q15305047) hydrogen (Q556): maintained by WikiProject (P6104): WikiProject Energy (Q15106301) hydrogen (Q556): maintained by WikiProject (P6104): WikiProject Materials (Q88425338) hydrogen (Q556): maintained by WikiProject (P6104): WikiProject Physics (Q8487193) hydrogen (Q556): maintained by WikiProject (P6104): WikiProject Biology (Q6723002) hydrogen (Q556): native label (P1705): lb:Waasserstoff hydrogen (Q556): native label (P1705): fr:Hydrogène hydrogen (Q556): schematic (P5555): Electron shell de 001 Wasserstoff.svg hydrogen (Q556): specific heat capacity (P2056): +14304.0 joule per kilogram kelvin hydrogen (Q556): standard enthalpy of formation (P3078): +217.998 kilojoule per mole hydrogen (Q556): standard molar entropy (P3071): +114.717 joule per mole kelvin hydrogen (Q556): has characteristic (P1552): diamagnetism (Q201048)
Querying chemical element data with SPARQL.
Request and response request · response (0.9 k chars)
{
"entity_id": "Q10801007",
"include_external_ids": false
}
group 1 (Q10801007): description: group of chemical elements group 1 (Q10801007): aliases: hydrogen group, group 1 elements, hydrogen and alkali metals, group 1 element group 1 (Q10801007): instance of (P31): group (Q83306) group 1 (Q10801007): instance of (P31): main group (Q428830) group 1 (Q10801007): has part(s) (P527): chemical element (Q11344) group 1 (Q10801007): has part(s) (P527): hydrogen (Q556) group 1 (Q10801007): has part(s) (P527): alkali metal (Q19557) group 1 (Q10801007): part of (P361): periodic table (Q10693) group 1 (Q10801007): part of (P361): s-block (Q208106) group 1 (Q10801007): followed by (P156): alkaline earth metal (Q19563) group 1 (Q10801007): topic's main category (P910): Category:Periodic table group 1 (Q8970417) group 1 (Q10801007): Commons category (P373): Periodic table group 1 group 1 (Q10801007): image (P18): Periodieksysteemmaardaneffeietsanders-kolom1.svg
Building a SPARQL query about groups and time periods.
Request and response request · response (0.7 k chars)
{
"entity_id": "Q191936",
"include_external_ids": false
}
period 1 (Q191936): description: first row (or period) of the periodic table of the chemical elements period 1 (Q191936): aliases: period 1 element period 1 (Q191936): Commons category (P373): Periodic table row 1 period 1 (Q191936): instance of (P31): period (Q101843) period 1 (Q191936): topic's main category (P910): Category:Periodic table row 1 (Q9067848) period 1 (Q191936): followed by (P156): period 2 (Q207712) period 1 (Q191936): has part(s) (P527): chemical element (Q11344) period 1 (Q191936): image (P18): Hydrogen discharge tube.jpg period 1 (Q191936): image (P18): Helium discharge tube.jpg period 1 (Q191936): on focus list of Wikimedia project (P5008): Wikipedia:Vital articles/Level/4 (Q6173448)
Request and response request · response (4.5 k chars)
{
"K": 130,
"sparql": "SELECT ?z ?sym ?eLabel ?groupLabel ?periodLabel WHERE {\n ?e wdt:P31 wd:Q11344 ;\n wdt:P1086 ?z ;\n wdt:P246 ?sym .\n OPTIONAL { ?e wdt:P361 ?group . ?group wdt:P31 wd:Q83306 . }\n OPTIONAL { ?e wdt:P361 ?period . ?period wdt:P31 wd:Q101843 . }\n SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }\n} ORDER BY ?z"
}
;sym;z;eLabel;groupLabel;periodLabel 0;H;1;hydrogen;group 1;period 1 1;He;2;helium;noble gases;period 1 2;Li;3;lithium;group 1;period 2 3;Be;4;beryllium;alkaline earth metal;period 2 4;B;5;boron;group 13;period 2 5;C;6;carbon;group 14;period 2 6;N;7;nitrogen;group 15;period 2 7;O;8;oxygen;group 16;period 2 8;F;9;fluorine;halogens;period 2 9;Ne;10;neon;noble gases;period 2 10;Na;11;sodium;group 1;period 3 11;Mg;12;magnesium;alkaline earth metal;period 3 12;Al;13;aluminium;group 13;period 3 13;Si;14;silicon;group 14;period 3 14;P;15;phosphorus;group 15;period 3 15;S;16;sulfur;group 16;period 3 16;Cl;17;chlorine;halogens;period 3 17;Ar;18;argon;noble gases;period 3 18;K;19;potassium;group 1;period 4 19;Ca;20;calcium;alkaline earth metal;period 4 20;Sc;21;scandium;group 3;period 4 21;Ti;22;titanium;group 4;period 4 22;V;23;vanadium;group 5;period 4 23;Cr;24;chromium;group 6;period 4 24;Mn;25;manganese;group 7;period 4 25;Fe;26;iron;group 8;period 4 26;Co;27;cobalt;group 9;period 4 27;Ni;28;nickel;group 10;period 4 28;Cu;29;copper;group 11;period 4 29;Zn;30;zinc;group 12;period 4 30;Ga;31;gallium;group 13;period 4 31;Ge;32;germanium;group 14;period 4 32;As;33;arsenic;group 15;period 4 33;Se;34;selenium;group 16;period 4 34;Br;35;bromine;halogens;period 4 35;Kr;36;krypton;noble gases;period 4 36;Rb;37;rubidium;group 1;period 5 37;Sr;38;strontium;alkaline earth metal;period 5 38;Y;39;yttrium;group 3;period 5 39;Zr;40;zirconium;group 4;period 5 40;Nb;41;niobium;group 5;period 5 41;Mo;42;molybdenum;group 6;period 5 42;Tc;43;technetium;group 7;period 5 43;Ru;44;ruthenium;group 8;period 5 44;Rh;45;rhodium;group 9;period 5 45;Pd;46;palladium;group 10;period 5 46;Ag;47;silver;group 11;period 5 47;Cd;48;cadmium;group 12;period 5 48;In;49;indium;group 13;period 5 49;Sn;50;tin;group 14;period 5 50;Sb;51;antimony;group 15;period 5 51;Te;52;tellurium;group 16;period 5 52;I;53;iodine;halogens;period 5 53;Xe;54;xenon;noble gases;period 5 54;Cs;55;caesium;group 1;period 6 55;Ba;56;barium;alkaline earth metal;period 6 56;La;57;lanthanum;group 3;period 6 57;Ce;58;cerium;;period 6 58;Pr;59;praseodymium;;period 6 59;Nd;60;neodymium;;period 6 60;Pm;61;promethium;;period 6 61;Sm;62;samarium;;period 6 62;Eu;63;europium;;period 6 63;Gd;64;gadolinium;;period 6 64;Tb;65;terbium;;period 6 65;Dy;66;dysprosium;;period 6 66;Ho;67;holmium;;period 6 67;Er;68;erbium;;period 6 68;Tm;69;thulium;;period 6 69;Yb;70;ytterbium;;period 6 70;Lu;71;lutetium;;period 6 71;Hf;72;hafnium;group 4;period 6 72;Ta;73;tantalum;group 5;period 6 73;W;74;tungsten;group 6;period 6 74;Re;75;rhenium;group 7;period 6 75;Os;76;osmium;group 8;period 6 76;Ir;77;iridium;group 9;period 6 77;Pt;78;platinum;group 10;period 6 78;Au;79;gold;group 11;period 6 79;Hg;80;mercury;group 12;period 6 80;Tl;81;thallium;group 13;period 6 81;Pb;82;lead;group 14;period 6 82;Bi;83;bismuth;group 15;period 6 83;Po;84;polonium;group 16;period 6 84;At;85;astatine;halogens;period 6 85;Rn;86;radon;noble gases;period 6 86;Fr;87;francium;group 1;period 7 87;Ra;88;radium;alkaline earth metal;period 7 88;Ac;89;actinium;group 3;period 7 89;Th;90;thorium;;period 7 90;Pa;91;protactinium;;period 7 91;U;92;uranium;;period 7 92;Np;93;neptunium;;period 7 93;Pu;94;plutonium;;period 7 94;Am;95;americium;;period 7 95;Cm;96;curium;;period 7 96;Bk;97;berkelium;;period 7 97;Cf;98;californium;;period 7 98;Es;99;einsteinium;;period 7 99;Fm;100;fermium;;period 7 100;Md;101;mendelevium;;period 7 101;No;102;nobelium;;period 7 102;Lr;103;lawrencium;;period 7 103;Rf;104;rutherfordium;group 4;period 7 104;Db;105;dubnium;group 5;period 7 105;Sg;106;seaborgium;group 6;period 7 106;Bh;107;bohrium;group 7;period 7 107;Hs;108;hassium;group 8;period 7 108;Mt;109;meitnerium;group 9;period 7 109;Ds;110;darmstadtium;group 10;period 7 110;Rg;111;roentgenium;group 11;period 7 111;Cn;112;copernicium;group 12;period 7 112;Nh;113;nihonium;group 13;period 7 113;Fl;114;flerovium;group 14;period 7 114;Mc;115;moscovium;group 15;period 7 115;Lv;116;livermorium;group 16;period 7 116;Ts;117;tennessine;halogens;period 7 117;Og;118;oganesson;noble gases;period 7 118;Uue;119;ununennium;group 1;period 8 119;Ubn;120;unbinilium;alkaline earth metal;period 8 120;Ubu;121;unbiunium;;period 8 121;Ubb;122;unbibium;;period 8 122;Ubt;123;unbitrium;;period 8 123;Ubq;124;unbiquadium;;period 8 124;Ubp;125;unbipentium;;period 8 125;Ubh;126;unbihexium;;period 8 126;Ubs;127;unbiseptium;;period 8 127;Ubo;128;unbioctium;;period 8 128;Ube;129;unbiennium;;period 8 129;Utn;130;untrinilium;;period 8
Designing a diagram layout with color-coded element blocks.
Wikidata returns 130 elements, including hypothetical period-8 entries (119–130, "ununennium" onward) that have never been synthesised. I'll exclude those and plot the 118 confirmed elements.
PubMed
And what tools does the Pubmed mcp offer?
The PubMed server wraps NCBI's PubMed/PMC APIs — seven tools, split between search/discovery and full-text retrieval.
Search and discovery
- search_articles — the main entry point: search PubMed for biomedical and life-sciences literature by keyword.
- lookup_article_by_citation — resolve a partial or informal citation (author, year, journal fragment) to the actual article.
- find_related_articles — pull related articles and resources for a given paper, useful for tracing a citation neighbourhood.
- get_research_guides (actually part of the Public MCP library server, not PubMed — worth noting the two are separate servers even though they sit next to each other in the tool list)
Article details
- get_article_metadata — title, authors, journal, abstract, publication date, and other bibliographic fields for a given ID.
- convert_article_ids — cross-walk between PMID, PMCID, and DOI, since different tools and citations use different identifier systems.
- get_copyright_status — copyright/licensing info for a given article, relevant before quoting or reproducing text.
Full text
- get_full_text_article — retrieve the full text from PubMed Central, when the article is open-access or otherwise available there. Not every PubMed-indexed article has a PMC full-text version — many are abstract-only unless the publisher has deposited the full text.
The natural workflow is search_articles → get_article_metadata (or find_related_articles to branch out) → convert_article_ids if you need a DOI for something else → get_full_text_article if it's open access, checking get_copyright_status before reproducing anything at length.
Given the copyright constraints I work under, I can search this, summarize abstracts, and point you to sources, but I'd paraphrase rather than reproduce full-text passages even when they're openly available. Want me to try a search on a topic to show it end to end?
Search for articles from the last two years on lead poisoning and retrieve their DOIs
Request and response request · response (0.5 k chars)
{
"query": "lead poisoning",
"date_from": "2023/09",
"date_to": "2025/09",
"max_results": 15,
"sort": "pub_date"
}
{
"pmids": [
"42500346",
"39564296",
"40580183",
"41800321",
"40865688",
"40939991",
"42344647",
"40982064",
"40953824",
"40737801",
"40679190",
"40662447",
"40638062",
"40615769",
"40512368"
],
"total_count": 775,
"returned_count": 15,
"query": "lead poisoning",
"query_translation": "(\"lead poisoning\"[MeSH Terms] OR (\"lead\"[All Fields] AND \"poisoning\"[All Fields]) OR \"lead poisoning\"[All Fields]) AND 2023/09/01:2025/09/30[Date - Publication]",
"has_more": true
}
775 total hits over two years — I'll pull metadata (with DOIs) for this first batch of 15, sorted most recent first.
Request and response request · response (8.1 k chars)
{
"pmids": [
"42500346",
"39564296",
"40580183",
"41800321",
"40865688",
"40939991",
"42344647",
"40982064",
"40953824",
"40737801",
"40679190",
"40662447",
"40638062",
"40615769",
"40512368"
]
}
{
"articles": [
{
"identifiers": {
"pmid": "42500346",
"pmc": "PMC13396321",
"doi": "10.1007/s12291-024-01271-3",
"pii": "1271"
},
"title": "Occupational Exposure to Lead Increases Inflammation that Causes Cardiovascular Disease and Lowers Selenium levels.",
"abstract": "Lead is a hazardous heavy metal. It has serious health effects due to its unique physical and chemical properties. Lead poisoning can induce inflammatory signaling pathways and oxidative stress. This course decreases resistance to diseases and infections. This study investigates the risks of occupational exposure to Lead in petroleum industry workers and its effect on the increase of high-sensitivity reactive protein (hs-CRP), which indicates an increased risk of heart disease. The study also investigated the antagonistic relationship between Lead and Selenium, as the latter is an important component of the antioxidant defense system. The study included two groups of males: the first group included ninety individuals working in oil well sites in Basrah, and the second group included ninety individuals who were not exposed. The results indicate high levels of toxic Lead, ≥ 0.0001, and a significant increase in high-sensitivity C-reactive protein (hs-CRP), ≥ 0.0001, in the worker's serum compared to the control group. In addition, there was a decrease in Selenium levels ( ≥ 0.001) in the worker's blood compared with the control group. High-sensitivity C-reactive protein (hs-CRP) was measured using a device (Finecare Plus, FS-112/FS-113/FS-205, Wondfo, China). Lead and Selenium levels were determined using an ICP-OES device (HORIBA Scientific, JY2000, France).\n\nExplaining the mechanism of lead toxicity.\n\nThe online version contains supplementary material available at 10.1007/s12291-024-01271-3.",
"doi": "10.1007/s12291-024-01271-3",
"journal": {
"title": "Indian journal of clinical biochemistry : IJCB",
"iso_abbreviation": "Indian J Clin Biochem"
},
"authors": [
{
"last_name": "Kadam",
"fore_name": "Nagham J",
"initials": "NJ",
"affiliations": [
"Department of Chemistry, College of Sciences, University of Basrah, Basrah, 61004 Iraq.",
"Iraqi Ministry of Education, The Basrah Education Directorate, Baghdad, Iraq."
]
},
{
"last_name": "Awad",
"fore_name": "Nadhum A N",
"initials": "NAN",
"affiliations": [
"Department of Chemistry, College of Sciences, University of Basrah, Basrah, 61004 Iraq."
]
},
{
"last_name": "Al-Taher",
"fore_name": "Saad Shaheen Hamadi",
"initials": "SSH",
"affiliations": [
"Internal Medicine, College of Medicine, University of Basrah, Basrah, 61004 Iraq."
]
}
],
"publication_date": {
"year": "2024",
"month": "11",
"day": "18"
},
"keywords": [
"Cardiovascular health",
"High-sensitivity C-reactive protein",
"Inflammation",
"Lead",
"Occupational health",
"Selenium"
],
"article_types": [
"Journal Article"
],
"language": "eng",
"citation": {
"volume": "41",
"issue": "4",
"pages": "626-632"
}
},
{
"identifiers": {
"pmid": "39564296",
"pmc": "PMC11572525",
"doi": "10.1177/02537176241255046",
"pii": "10.1177_02537176241255046"
},
"title": "A New Source of Opioid and Lead Toxicity on the Block-Kamini: An Emerging Health Hazard-A Case Series.",
"abstract": "Illicit opioid use through the Ayurvedic productmay cause lead poisoning and refractory anemia due to lead adulteration. We retrospectively describe three opioid-dependent men with abdominal pain, anemia, elevated blood lead levels, and basophilic stippling who improved after chelation therapy, highlighting the need for early recognition and lead screening.",
"doi": "10.1177/02537176241255046",
"journal": {
"title": "Indian journal of psychological medicine",
"iso_abbreviation": "Indian J Psychol Med"
},
"authors": [
{
"last_name": "Abhilasha",
"fore_name": "Pallavi",
"initials": "P",
"affiliations": [
"Dept. of Psychiatry, Christian Medical College, Ludhiana, Punjab, India."
]
},
{
"last_name": "Vats",
"fore_name": "Kashika",
"initials": "K",
"affiliations": [
"Dept. of Hematology, Christian Medical College, Ludhiana, Punjab, India."
]
},
{
"last_name": "Gautam",
"fore_name": "Nikhil",
"initials": "N",
"affiliations": [
"Dept. of Psychiatry, Christian Medical College, Ludhiana, Punjab, India."
]
},
{
"last_name": "Kaur",
"fore_name": "Vanipreet",
"initials": "V",
"affiliations": [
"Dept. of Psychiatry, Christian Medical College, Ludhiana, Punjab, India."
]
},
{
"last_name": "Singla",
"fore_name": "Mamta",
"initials": "M",
"affiliations": [
"Dept. of Psychiatry, Christian Medical College, Ludhiana, Punjab, India."
]
},
{
"last_name": "Pavitra Ds",
"affiliations": [
"Dept. of Hematology, Christian Medical College, Ludhiana, Punjab, India."
]
},
{
"last_name": "Gupta",
"fore_name": "Manasi",
"initials": "M",
"affiliations": [
"Dept. of Hematology, Christian Medical College, Ludhiana, Punjab, India."
]
},
{
"last_name":
… [58155 characters omitted from the middle; 66155 in total] …
"Acute Kidney Injury",
"Lead Poisoning",
"Renal Insufficiency, Chronic",
"Plant Extracts",
"Oxidative Stress",
"Kidney Diseases"
],
"article_types": [
"Journal Article",
"Review"
],
"language": "eng",
"citation": {
"volume": "204",
"issue": "1",
"pages": "495-513"
}
}
],
"count": 15,
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}
According to PubMed, here are the results from a search for "lead poisoning," restricted to the last two years (Sept 2023–Sept 2025), sorted by publication date. The search returned 775 total matches — this is the most recent page of 15, with DOIs:
| Title | Journal | Date | DOI |
|---|---|---|---|
| Hepatoprotective effect of pyridoxal phosphate against lead poisoning by inhibiting Kupffer cell hyperplasia... | Naunyn-Schmiedeberg's Arch Pharmacol | 2025-09-22 | 10.1007/s00210-025-04232-6 |
| Prevention of Lead Exposure in the Perinatal Period | J Obstet Gynecol Neonatal Nurs | 2025-09-13 | 10.1016/j.jogn.2025.08.005 |
| A Patient With Liver Injury, Portal Hypertension, Anemia, and Peripheral Neuropathy (lead poisoning case) | Gastroenterology | 2025-08-25 | 10.1053/j.gastro.2025.08.023 |
| Rapid detection of lead(II) with colorimetric optical sensors... urinary lead | Talanta | 2025-07-25 | 10.1016/j.talanta.2025.128635 |
| EU regulation: protecting children's and wildlife health from lead ammunition | Ambio | 2025-07-10 | 10.1007/s13280-025-02219-4 |
| Protective effects of Lactobacillus curvatus against acute lead and cadmium toxicity in Caspian Kutum | Biol Trace Elem Res | 2025-07-04 | 10.1007/s12011-025-04677-z |
| Lead-Induced Nephrotoxicity and Its Therapeutic Interventions: An Updated Review | Biol Trace Elem Res | 2025-06-13 | 10.1007/s12011-025-04692-0 |
| Occupational Exposure to Lead Increases Inflammation... and Lowers Selenium Levels | Indian J Clin Biochem | 2024-11-18 | 10.1007/s12291-024-01271-3 |
| A New Source of Opioid and Lead Toxicity — "Kamini" Ayurvedic product | Indian J Psychol Med | 2024-06-09 | 10.1177/02537176241255046 |
Three results the search surfaced under the broader "poisoning" match but that aren't actually about lead — worth flagging rather than silently dropping: a diquat/herbicide case (10.3389/fpubh.2025.1729214), a carbon monoxide/hyperbaric oxygen study (10.4103/mgr.MEDGASRES-D-25-00109), and a paraquat poisoning case (10.4103/ajts.ajts_219_24), plus two more unrelated to lead specifically (a heavy-metals-mixture/depression study that includes lead as one of several metals, an MRSA food-safety review, and a benzodiazepine/opioid overdose study — PubMed's Boolean OR on "lead" AND "poisoning" catches adjacent topics).
This is one page of 15 out of 775 — I can page through more (retstart) or narrow the query (e.g., restricting to [Title], or adding NOT paraquat NOT diquat to cut the herbicide-poisoning noise) if you want a cleaner or larger set.