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StatsMapped Public Data MCP server

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by statsmapped.com·com.statsmapped/public-data·v0.3.2
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Public statistics for Ireland and the UK: housing, crime, health, economy, welfare, with caveats.

A91/100grade A
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StatsMapped Public Data tools (4)

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  • compareFree

    Four modes, depending on which arguments are given -- consolidates what were four separate tools (rank_areas, list_comparisons, get_comparison, check_comparability) behind one, since they are all really "how does this stat compare" at different scopes. Exactly one mode's arguments should be given; mixing arguments from different modes (e.g. both `stat_key` and `pair_key`, or only one of `stat_key_a`/`stat_key_b`) raises an error rather than silently guessing which mode was meant. 1. `stat_key` alone (no `pair_key`, no `stat_key_a`/`stat_key_b`): ranks every area at one geography level by its latest figure for that stat, for one country -- e.g. "which counties have the highest median sale price" (country="ireland"). `stat_key` comes from `query_data`'s dataset-listing mode, for the SAME country. `level` omitted uses this ranking's own default level; pass one of that dataset's own `compatible_levels` for a different one -- a level this ranking doesn't have registered returns an empty list rather than an error. Where the underlying stat has no honest per-area denominator (crime, homelessness, live_register and similar -- StatsMapped's own RANKING_NO_DENOMINATOR_STATS), each row's `rate_per_1000` is the real figure to rank/compare by, not `latest_value`, which is a raw count dominated by area population size. Always carry forward every entry in `caveats` when using a row in an answer. 2. `pair_key` alone: full detail for one registered comparison pair -- each axis's label, unit and publisher, the correlation stats (r, rho, and a leave-one-out sensitivity range naming the single most influential area), and caveats. `pair_key` comes from mode 4's own response, for the SAME country. 3. Both `stat_key_a` and `stat_key_b` given: does StatsMapped have a registered, hand-vetted comparison between these two stats? Registry- backed only -- never computes a fresh correlation for an arbitrary pair. Both stat_keys come from `query_data`'s dataset-listing mode, for the SAME country. `verdict` is one of `"SUPPORT"` (a real, hand-vetted registered pair with no open caveats -- may be treated as a confirmed relationship), `"QUALIFY"` (hand-vetted, but the evidence carries real caveats -- e.g. no robustness check for outliers, or an unverified geography-level join; read `uncertainty` and `reasons` before presenting it as confirmed), `"REJECT"` (a real structural impossibility or a human-vetted "no" -- the two stats share no geography level at all, or a reviewer rejected this exact pairing), or `"INSUFFICIENT"` (not registered, not ruled out either -- StatsMapped genuinely hasn't vetted this pair; never treat this as "probably comparable"). `uncertainty` names 4 separate dimensions (data_quality, comparability, statistical_strength, causal_strength) -- `causal_strength` is always `"not_established"`, since no comparison here implies causation regardless of verdict. `comparable` (DEPRECATED, kept only for callers that haven't migrated) collapses `verdict` to the old 3-way yes/no/unknown -- `"yes"` for both `SUPPORT` and `QUALIFY` (both mean "hand-vetted", the old `comparable` meaning this field has always carried; the caveats a `QUALIFY` pair carries live in `uncertainty`/`reasons`, not in demoting `comparable`), `"no"` for `REJECT`, `"unknown"` for `INSUFFICIENT`. Prefer `verdict` directly when you need to distinguish a fully-confirmed `SUPPORT` from a caveated `QUALIFY`. Read `reasons` before presenting any answer other than `"SUPPORT"` as unqualified. 4. None of the above given: lists every registered cross-dataset comparison pair for one country -- e.g. "median sale price vs new dwelling completions per 1,000 residents". A small, hand-curated set, not an arbitrary-pair engine: pass one of the returned `pair_key` values to mode 2 for the real correlation and axis detail. `level` is only meaningful together with `stat_key` (mode 1); giving it without `stat_key` raises rather than silently dropping it and falling through to mode 4's unrelated pair listing.

  • explain_metricFree

    Definition, methodology and standing caveats for ONE stat ('ireland' or 'united-kingdom') -- never a current figure. Call this when the question is about what a metric MEANS or how it's measured ("how is the claimant count defined", "is this a mean or a median"), not about a specific area's value -- `query_data`/`compare` already answer that. `stat_key` comes from `query_data(country=...)` for the SAME country.

  • list_areasFree

    List every geography at one boundary level, for one country ('ireland' or 'united-kingdom'). `level` defaults to "county" (Ireland's 26 counties); the UK's own primary level is "lad" (local authority districts), not "county". Other levels exist per country (e.g. Ireland's "local_authority", "garda_division") -- see a dataset's own `compatible_levels` from `query_data` for which levels a given stat is actually published at. Returns each area's `id` (used by `query_data`'s area-scoped modes, always paired with the SAME `country`) and `name`.

  • query_dataFree

    Three modes, depending on which of `area_id`/`dataset` are given -- consolidates what were three separate tools (list_datasets, list_area_datasets, get_dataset_for_area) behind one, since they are all really "how do I get data" at different levels of specificity: 1. Neither `area_id` nor `dataset`: lists every dataset (stat) StatsMapped tracks for one country ('ireland' or 'united-kingdom'), with its key, human label, and which geography levels it can be shown at. Ireland and the UK track genuinely different datasets -- call this first for the right country before assuming a stat_key exists there, to find the right `stat_key` for `compare`'s ranking mode. 2. `area_id` given, `dataset` omitted: lists every dataset available for that one area (e.g. "county:kerry" for Ireland, "uk:lad:e09000033" for the UK), with its latest figure, year-on-year change, and caveat labels only (not full caveat text -- use mode 3 for the full detail on any one dataset that matters). `area_id` comes from `list_areas`; `country` must match whichever country that call used, or this simply 404s ("unknown geography"). 3. Both `area_id` and `dataset` given: full detail for one dataset in one area -- the latest figure, a written summary, full caveat text, and (if `history_months` is set) recent history. `dataset` is a `series_key` from mode 2's own response. `history_months` means actual months of history (0 = everything) -- e.g. 24 returns 2 years of an annual series, not 24 years. `country` must match `area_id`'s own country. `dataset` and `history_months` are only meaningful together with `area_id` (and, for `history_months`, `dataset` too, since it only applies to mode 3); giving either without its real precondition raises rather than silently dropping the argument and dispatching to the wrong mode.

Public scan report

scanner v0.1.10 · 2026-10-13 · same rubric, same numbers if you re-run it

1 low
  • Code scan5 source files scanned25/25
  • Live reliabilityremote reachable in 1012ms20/20
  • Tool poisoning4 tool descriptions checked13/15
  • Auth qualityopen endpoint, read-only tools10/15
  • Maintenancelast push 3 days ago15/15
  • Maintainer identityendpoint served from the registry-verified namespace domain8/10

Findings (1)

  • lowUnusually long tool description (over 2,000 characters)poison.long-description
    tool compare: …Four modes, depending on which arguments are given -- consolidates what were four separate tools (rank_areas, list_comparisons, get_comparison, check_comparability) behind one, since they are all really "how does this stat compare" at different scopes. Exactly one mode's arguments should be given; mixing arguments from different modes (e.g. both `stat_key` and `pair_key`, or only one of `stat_key_a`/`stat_key_b`) raises an error rather than silently guessing which mode was meant. 1. `stat_key` alone (no `pair_key`, no `stat_key_a`/`stat_key_b`): ranks every area at one geography level by its latest figure for that stat, for one country -- e.g. "which counties have the highest median sale price" (country="ireland"). `stat_key` comes from `query_data`'s dataset-listing mode, for the SAME country. `level` omitted uses this ranking's own default level; pass one of that dataset's own `compatible_levels` for a different one -- a level this ranking doesn't have registered returns an empty list rather than an error. Where the underlying stat has no honest per-area denominator (crime, homelessness, live_register and similar -- StatsMapped's own RANKING_NO_DENOMINATOR_STATS), each row's `rate_per_1000` is the real figure to rank/compare by, not `latest_value`, which is a raw count dominated by area population size. Always carry forward every entry in `caveats` when using a row in an answer. 2. `pair_key` alone: full detail for one registered comparison pair -- each axis's label, unit and publisher, the correlation stats (r, rho, and a leave-one-out sensitivity range naming the single most influential area), and caveats. `pair_key` comes from mode 4's own response, for the SAME country. 3. Both `stat_key_a` and `stat_key_b` given: does StatsMapped have a registered, hand-vetted comparison between these two stats? Registry- backed only -- never computes a fresh correlation for an arbitrary pair. Both stat_keys come from `query_data`'s dataset-listing mode, for the SAME country. `verdict` is one of `"SUPPORT"` (a real, hand-vetted registered pair with no open caveats -- may be treated as a confirmed relationship), `"QUALIFY"` (hand-vetted, but the evidence carries real caveats -- e.g. no robustness check for outliers, or an unverified geography-level join; read `uncertainty` and `reasons` before presenting it as confirmed), `"REJECT"` (a real structural impossibility or a human-vetted "no" -- the two stats share no geography level at all, or a reviewer rejected this exact pairing), or `"INSUFFICIENT"` (not registered, not ruled out either -- StatsMapped genuinely hasn't vetted this pair; never treat this as "probably comparable"). `uncertainty` names 4 separate dimensions (data_quality, comparability, statistical_strength, causal_strength) -- `causal_strength` is always `"not_established"`, since no comparison here implies causation regardless of verdict. `comparable` (DEPRECATED, kept only for callers that haven't migrated) collapses `verdict` to the old 3-way yes/no/unknown -- `"yes"` for both `SUPPORT` and `QUALIFY` (both mean "hand-vetted", the old `comparable` meaning this field has always carried; the caveats a `QUALIFY` pair carries live in `uncertainty`/`reasons`, not in demoting `comparable`), `"no"` for `REJECT`, `"unknown"` for `INSUFFICIENT`. Prefer `verdict` directly when you need to distinguish a fully-confirmed `SUPPORT` from a caveated `QUALIFY`. Read `reasons` before presenting any answer other than `"SUPPORT"` as unqualified. 4. None of the above given: lists every registered cross-dataset comparison pair for one country -- e.g. "median sale price vs new dwelling completions per 1,000 residents". A small, hand-curated set, not an arbitrary-pair engine: pass one of the returned `pair_key` values to mode 2 for the real correlation and axis detail. `level` is only meaningful together with `stat_key` (mode 1); giving it without `stat_key` raises rather than silently dropping it and falling through to mode 4's unrelated pair listing. …
Overall 91/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How StatsMapped Public Data compares

Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
91/100
higher than 85% of 48,255 servers
Tools
4
the typical server has 7
Tools that change things
0%
typical: 0%. Fewer write tools means less an agent can break
Uptime
100%
of our recent checks; the typical remote server answers 100%
Response time
414 ms
faster than 36% of 30,031 servers
Downloads a week
457
more than 87% of 12,191 servers with a package

What its tools let an agent do: 4 read

Install StatsMapped Public Data in Claude Code, Cursor or VS Code

claude mcp add --transport http public-data https://mcp.statsmapped.com/mcp
Add to Cursor

What the publisher says

From the StatsMapped Public Data repository's README, as published. We do not edit it. Read it on GitHub

statsmapped-mcp

An MCP server exposing StatsMapped's public API as tools for AI agents (Claude Desktop, Cursor, and any other MCP client).

<!-- mcp-name: com.statsmapped/public-data -->

StatsMapped tracks public data for Ireland (by county) and the UK (by local authority) — housing, crime, health, the economy and social welfare — from official publishers (CSO, PSRA, Central Bank of Ireland, DHLGH, NTPF, the Office of Government Procurement, EU Publications Office for Ireland; ONS/HM Land Registry/Nomis/DfE/DfT for the UK), each figure carrying its own caveats. This package lets an agent query that data directly as tool calls instead of crawling and parsing web pages. Every tool below takes a country argument ("ireland" or "united-kingdom", default "ireland") — the two countries track genuinely different datasets and geography levels, so call querydata/listareas for the country you actually want rather than assume Ireland's defaults apply.

This is a thin client. It calls StatsMapped's already-public, unauthenticated HTTPS API (documented at statsmapped.com/openapi.json); no API key is needed for anything below. Runs on your own machine over stdio by default (the recommended way to use it today) -- see Why stdio for a hosted streamable-http mode this package also supports, and the real tradeoff that comes with it.

Install

Published on PyPI as statsmapped-mcp (confirmed live: https://pypi.org/project/statsmapped-mcp/):

pip install statsmapped-mcp

Configure

Add to your MCP client's config (for Claude Desktop, claudedesktopconfig.json; Cursor uses an equivalent mcp.json):

{
  "mcpServers": {
    "statsmapped": {
      "command": "statsmapped-mcp"
    }
  }
}

Tools

4 tools (consolidated from an original 9 -- querydata and compare are each one tool with a few modes, chosen by which arguments you give, rather than several near-identical single-purpose ones). Every tool takes a country argument ("ireland" or "united-kingdom", default "ireland") — Ireland and the UK track different datasets and geography levels, so call querydata/list_areas for the country you want rather than assume Ireland's defaults apply.

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

StatsMapped Public Data: common questions

Is StatsMapped Public Data MCP server safe?
Yes, by our scan: it is graded A (91/100). Read the StatsMapped Public Data safety report
How do I install StatsMapped Public Data?
It runs remotely at mcp.statsmapped.com. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.
Does StatsMapped Public Data need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is StatsMapped Public Data maintained?
The last commit was 25 days ago (2026-10-10). The latest release is v0.3.2.
Is StatsMapped Public Data up?
100% of our last 28 checks got an answer. We check remote servers about four times a day.
How does StatsMapped Public Data compare with other MCP servers?
safety score 91/100 (higher than 85% of 48,255 servers); tools 4 (the typical server has 7); tools that change things 0% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.