Server data from the Official MCP Registry
Public statistics for Ireland and the UK: housing, crime, health, economy, welfare, with caveats.
About
Public statistics for Ireland and the UK: housing, crime, health, economy, welfare, with caveats.
Remote endpoints: streamable-http: https://mcp.statsmapped.com/mcp
Security Report
This is a thin, read-only client for StatsMapped's public unauthenticated API, and its permissions broadly match a data-query server. The main concern is undisclosed usage telemetry: each tool call posts the tool name, status, and a persistent per-install UUID stored in the home directory, and the README does not mention this. Supply chain analysis found 5 known vulnerabilities in dependencies (0 critical, 2 high severity). Package verification found 1 issue.
4 files analyzed · 10 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Required
This plugin requests these system permissions. Most are normal for its category.
How to Install & Connect
Available as Local & Remote
This plugin can run on your machine or connect to a hosted endpoint. during install.
Documentation
View on GitHubFrom the project's GitHub README.
statsmapped-mcp
An MCP server exposing StatsMapped's public API as tools for AI agents (Claude Desktop, Cursor, and any other MCP client).
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 query_data/list_areas 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, claude_desktop_config.json; Cursor uses an
equivalent mcp.json):
{
"mcpServers": {
"statsmapped": {
"command": "statsmapped-mcp"
}
}
}
Tools
4 tools (consolidated from an original 9 -- query_data 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
query_data/list_areas for the country you want rather than assume Ireland's defaults apply.
query_data(area_id=None, dataset=None, history_months=0, country="ireland")— three modes:- neither
area_idnordataset: every stat StatsMapped tracks for one country, with its key, label, and which geography levels it's published at. Start here. area_idgiven,datasetomitted: every dataset available for one area (e.g."county:kerry"for Ireland,"uk:lad:e09000033"for the UK), with its latest figure and year-on-year change. Caveats are projected to label + severity only, not full text — the point is deciding which datasets matter before paying for the full detail on any one of them.- both
area_idanddatasetgiven: full detail on one dataset in one area — a written summary, full caveat text, and (optionally, viahistory_months) recent history.
- neither
list_areas(level="county", country="ireland")— every geography at one boundary level.leveldefaults to Ireland's 26 counties; the UK's own primary level is"lad"(local authority districts), not"county"— other levels exist per country too (Ireland'slocal_authority/garda_divisionamong them).compare(stat_key=None, level=None, pair_key=None, stat_key_a=None, stat_key_b=None, country="ireland")— four modes, exactly one set of arguments at a time (mixing them raises an error):stat_keyalone: every area at one level, ranked by its latest figure for that stat, highest first.pair_keyalone: full detail for one registered comparison pair — each axis's label/unit/publisher, the correlation stats (r, rho, a leave-one-out sensitivity range), and caveats.pair_keycomes from the no-argument mode below.- both
stat_key_aandstat_key_b: does StatsMapped have a registered, hand-vetted comparison between these two stats? Registry-backed only — never computes a fresh correlation for an arbitrary pair.comparableis a string, not a bool:"yes"(a registered, hand-vetted pair;pair_keynames it),"no"(the two stats share no geography level at all), or"unknown"(not registered, but not ruled out: treat as "ask a human", not "probably yes")."unknown"is the normal result for most pairs. Compare the value explicitly (result["comparable"] == "yes"); a truthiness test is true for all three.reasons(a list) always explains the answer. - none of the above: 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.
explain_metric(stat_key, country="ireland")— definition, methodology and standing caveats for one stat, never a current figure. Use this when the question is about what a metric means or how it's measured, not about one area's value.
Why stdio, not a hosted server, by default
StatsMapped runs on a single free-tier instance. A remote MCP endpoint hosted there would let an agent's own multi-area query pattern (calling the same tool once per area, in a loop) reproduce exactly the load pattern that has already caused timeouts on that instance under a large geography fan-out. Running over stdio means every call goes through your own network connection to the same public HTTPS API this package's tools call directly, with no shared bottleneck -- each user's own machine makes the HTTP calls, so N users' traffic is naturally spread across N source IPs, not funnelled through one.
server.py also supports a real hosted streamable-http mode (MCP_TRANSPORT=streamable-http)
for a deployment that accepts that tradeoff -- StatsMapped's public API is itself rate-limited
per source IP (600 requests/hour), but a hosted MCP endpoint proxies every remote user's calls
through ONE shared egress IP, so all remote users of a hosted endpoint would share that one
bucket rather than each getting their own. A StatsMapped-hosted endpoint is live at
https://mcp.statsmapped.com/mcp (Streamable HTTP) -- confirmed responding correctly, no
separate install needed for a client that speaks Streamable HTTP directly. The bare host
(https://mcp.statsmapped.com, no path) redirects to the same endpoint as a courtesy.
Development
cd mcp-server
pip install -e .
python tests/test_client.py
The test suite runs against the real live API (https://statsmapped.com by default, or
STATSMAPPED_MCP_BASE_URL if set) — read-only GETs only, nothing here writes any data or needs
a key.
Licence
MIT for this package. The underlying data keeps each publisher's own licence — see
statsmapped.com/ireland/sources for Ireland's own
publisher/licence detail before reusing any figure outside of querying it through an agent (a UK
equivalent page doesn't exist yet — check each UK tool response's own caveats/sources fields
in the meantime).
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