ILO Statistics (ILOSTAT) MCP Server
README.md
# ILO Labour Statistics (ILOSTAT) — MCP Server
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🇧🇷 [Leia em Português](https://github.com/SidneyBissoli/ilo-mcp-server/blob/main/README.pt-BR.md)
A **public, hosted, provenance-first** [MCP](https://modelcontextprotocol.io) server for the
**International Labour Organization (ILO)** statistics — the **ILOSTAT** database —
**no installation, no account, no API key**. Point your MCP client at the hosted endpoint and
ask about unemployment, employment, wages, working time and other labour indicators by
country, year, sex and age. It runs on Cloudflare Workers over Streamable HTTP and talks to
the official ILOSTAT SDMX REST API.
> **Independent project.** This is an unofficial, community-built client of the ILO's public
> ILOSTAT API — not affiliated with or endorsed by the International Labour Organization.
> Data remain © ILO under CC BY 4.0; see [Data license and attribution](#data-license-and-attribution).
Every response carries a **provenance block** (source URL, data vintage, real retrieval
timestamp, license, ILO citation) — exact figures with an audit trail, not numbers guessed
from training data.
> 🇧🇷 **Em português.** Servidor MCP **remoto e hospedado** (nada para instalar, sem conta e sem
> chave) para as estatísticas de **mercado de trabalho** da OIT — desemprego, emprego, salários,
> jornada e informalidade por país, ano, sexo e idade, direto no Claude, no ChatGPT ou em
> qualquer cliente MCP, com proveniência e citação da fonte em cada resposta:
> [README em português](https://github.com/SidneyBissoli/ilo-mcp-server/blob/main/README.pt-BR.md).
## Questions it answers
In plain language, inside the MCP client — the assistant picks the tool and the filters:
- "What has happened to unemployment in Brazil since 2015?" (`ilo_get_data`)
- "Compare youth unemployment in Brazil, Mexico and South Africa." (`ilo_compare_countries`)
- "How large is the gender pay gap, and where does the ILO publish it?"
(`ilo_search_indicators` → `ilo_get_data`)
- "What share of employment in India is informal?" (`ilo_search_indicators` → `ilo_get_data`)
- "Which ILOSTAT dataflow has average monthly earnings by sex and economic activity?"
(`ilo_search_indicators`)
- "Which country, age and sex codes can I filter this indicator by?" (`ilo_list_dimension_values`)
- "Give me a labour-market profile of Viet Nam." (`ilo_country_labour_profile`)
**Ask in your words, not the ILO's.** ILOSTAT is worded in British statistical English, and the
catalogue is matched on the dataflow name — so the everyday or US word used to return *nothing at
all*. Measured over the 1,212 dataflows of the official catalogue (2026-09-13), and fixed since
0.6.0: the search translates the term and tells you it did.
| you ask | hits before | ILOSTAT writes | hits |
| --- | ---: | --- | ---: |
| `labor`, `labor force` | 0 | labour, labour force | 176, 122 |
| `wages`, `salary` | 0 | earnings | 107 |
| `informality` | 0 | informal | 133 |
| `gender` | 2 | sex | 1,131 |
| `productivity` | 0 | output per worker | 4 |
| `jobless` | 0 | unemployment | 108 |
## Comparison with the alternatives
Anyone who already works with ILOSTAT has good tools, and this server **replaces none of them** —
it sits somewhere else in the chain: it answers the question at the point where the question is
asked, inside the assistant, with source, vintage and licence attached to the answer. Detail,
side-by-side examples and the measured numbers in
[`docs/alternatives.md`](https://github.com/SidneyBissoli/ilo-mcp-server/blob/main/docs/alternatives.md).
| Tool | What it is | When to prefer it |
| --- | --- | --- |
| **ilo-mcp-server** (this) | Remote MCP server, hosted, nothing to install: 6 tools over the ~1,200 ILOSTAT dataflows, with a provenance block per answer | The question is asked in an assistant (Claude, ChatGPT, Cursor, Claude Code) and the answer has to be auditable |
| [Rilostat](https://ilostat.github.io/Rilostat/) 2.5.0 (R, CRAN) | **The ILO's own R package**, written by ILO staff: bulk download, metadata, filtering and reshaping | You are in R and want the dataset in a data frame — a whole table, repeatedly, for analysis |
| [sdmx1](https://pypi.org/project/sdmx1/) 2.27.0 / [pandaSDMX](https://pypi.org/project/pandasdmx/) 1.10.0 (Python) | Generic SDMX clients; ILO is one of ~36 sources they know | Your pipeline is Python and you want SDMX objects, or the same code across several SDMX agencies |
| [DBnomics](https://db.nomics.world/ILO) (API, `dbnomics` for Python, [rdbnomics](https://cran.r-project.org/package=rdbnomics) 0.6.4 for R) | Aggregator that republishes 1,071 ILO datasets next to other providers, one API for all | You want ILO series alongside IMF, OECD, Eurostat in a single interface |
| [ILOSTAT SDMX REST API](https://ilostat.ilo.org/resources/sdmx-tools/) | The source itself, which this server calls | You are building your own client and want full control |
**Do not use this server when** you need a whole dataset rather than an answer (Rilostat's bulk
download is the right tool), when the question is not labour statistics published by the ILO
(education → UNESCO UIS, national accounts → IMF/World Bank), or when you need microdata: ILOSTAT
publishes aggregates, and so does this server.
**Sister servers**, same design and same provenance block, for other official sources:
[IBGE](https://ibge.sidneybissoli.com) (Brazilian statistics),
[BCB](https://bcb.sidneybissoli.com) (Central Bank of Brazil),
[Senado](https://senado.sidneybissoli.com) (Brazilian Senate open data),
[SIH/SUS](https://sih.sidneybissoli.com) (Brazilian hospital admissions) and
[medical terminologies](https://medical.sidneybissoli.com) (ICD-11, ICD-10, LOINC, RxNorm, ATC, MeSH).
## Use it (hosted — no setup)
Point any MCP client at the Streamable HTTP endpoint:
```
https://ilo.sidneybissoli.com/mcp
```
Claude Desktop / Claude Code and other clients with native remote support:
```json
{
"mcpServers": {
"ilostat": {
"url": "https://ilo.sidneybissoli.com/mcp"
}
}
}
```
For clients that launch MCP servers as a command, use the
[`mcp-remote`](https://www.npmjs.com/package/mcp-remote) bridge:
```json
{
"mcpServers": {
"ilostat": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://ilo.sidneybissoli.com/mcp"]
}
}
}
```
The `ilo-mcp-server.sidneybissoli.workers.dev` hostname is also served, as a secondary.
### ChatGPT (Deep Research)
ChatGPT deep research (and company knowledge, and research workflows over the Responses API) only uses an MCP server that exposes exactly `search` and `fetch` — this server does, on top of the `ilo_*` tools. Point the connector at the hosted endpoint, no key required:
```
https://ilo.sidneybissoli.com/mcp
```
`search` ranks the query against the full ILOSTAT dataflow catalogue (~1,200 SDMX dataflows — employment, unemployment, wages, working time, informality, SDG labour indicators) and returns `{ id, title, url }` (`ind:<DATAFLOW_ID>`, e.g. `ind:DF_UNE_2EAP_SEX_AGE_RT`); `fetch` returns the dataflow as readable Markdown — name, data vintage, dimensions and codelists, the ILO's default selection and how to query it with `ilo_get_data` — with the public ILOSTAT data explorer page as `url`, which is what ChatGPT cites. Both carry the same provenance block as every other tool, in `structuredContent` and `_meta` (the text channel is the contract's JSON). In ChatGPT's developer mode (Settings → Security and login → Developer mode) any tool is callable — the `ilo_*` tools remain the ones to use for data.
## Run locally (stdio)
Prefer not to route queries through a third-party host? The **same server** also runs as a
**local stdio process** that talks directly to the official ILOSTAT API — same 6 tools, resources and prompts,
same limits, same provenance block, no Cloudflare in the loop.
No install needed — the package is on npm ([`ilo-mcp-server`](https://www.npmjs.com/package/ilo-mcp-server), Node ≥ 20):
```json
{
"mcpServers": {
"ilostat": {
"command": "npx",
"args": ["-y", "ilo-mcp-server"]
}
}
}
```
Or from source:
```bash
git clone https://github.com/SidneyBissoli/ilo-mcp-server
cd ilo-mcp-server
npm install
npm run build
node dist/cli.js # serves MCP over stdio (Ctrl+C to stop)
```
(then point the client at `node /path/to/ilo-mcp-server/dist/cli.js`).
Differences from the hosted server, all due to the absence of Cloudflare bindings: the SDMX
cache lives in process memory (structures and codelists are reused within a session, not across
sessions); the search catalogue is downloaded from the official endpoint on the first search
(its real `retrieved_at` is reported in provenance); no usage metrics, rate limit or auth. Logs
go to **stderr** — stdout carries only the JSON-RPC stream. The repository `Dockerfile` builds
this runtime (used by the Glama registry).
## Tools
| Tool | What it does | Source |
|---|---|---|
| `ilo_search_indicators` | keyword search over ~1,210 dataflows (paginated by `offset`) | local catalogue (no upstream call) |
| `ilo_get_indicator_metadata` | dimensions, codelists, vintage and default selection of a dataflow | cached structure (miss → upstream) |
| `ilo_list_dimension_values` | valid codes of one dimension (paginated by `offset`) | cached codelist (miss → upstream) |
| `ilo_get_data` | observations filtered by dimension and period | 1 live REST call per query |
| `search` | ChatGPT Deep Research contract: ranks a query against the full dataflow catalogue, returns `{ id, title, url }` (`ind:<DATAFLOW_ID>`) | in-memory index built from the local catalogue (24 h) |
| `fetch` | ChatGPT Deep Research contract: one dataflow as readable Markdown with the public data explorer page as `url` | cached structure (miss → upstream) |
Typical flow: `ilo_search_indicators` → `ilo_get_indicator_metadata` / `ilo_list_dimension_values`
to discover valid filter codes → `ilo_get_data` with country and period filters.
Every response carries the **provenance block v1.0**
([`@sbissoli/mcp-provenance`](https://www.npmjs.com/package/@sbissoli/mcp-provenance), modes
`concise`/`detailed` via the `provenance_mode` parameter) on three channels:
`structuredContent`, namespaced `_meta` (`com.sidneybissoli.ilostat/*`) and a text footer.
## Resources and prompts
Three **resources** (static, `text/markdown`, no upstream call) that a client can attach to the
context before calling tools — they save the 2–3 discovery calls most sessions spend on
"which dataflow, which codes":
| URI | Content |
|---|---|
| `ilostat://guide` | tool workflow, stable code conventions (`REF_AREA` ISO3 + `X`-aggregates, `SEX`, `AGE`, `FREQ`, dataflow id suffixes), limits, reporting rules |
| `ilostat://reference/key-dataflows` | verified dataflow ids by topic (unemployment, employment, participation, wages, hours, informality, NEET, SDG 8, productivity) |
| `ilostat://reference/provenance` | meaning of every provenance field and how to cite the ILO |
Three **prompts** — ready-made workflows that chain the tools and end with the citation rules
(arguments are strings; period arguments optional):
| Prompt | Arguments | Result |
|---|---|---|
| `ilo_country_labour_profile` | `country`, `start_period`, `end_period` | labour-market profile of one country (unemployment, participation, employment ratio, informality, NEET, earnings, hours) |
| `ilo_compare_countries` | `countries`, `indicator`, `start_period`, `end_period` | comparison table across countries/aggregates in one data call, flagging modelled estimates vs reported data |
| `ilo_indicator_trend` | `indicator`, `country`, `start_period`, `end_period` | time series of one indicator with first/last, peak/trough and `OBS_STATUS` breaks |
Every dataflow id quoted in the resources and prompts is checked against the catalogue seed by
the test suite, so the documentation cannot point at an id the search would not find.
## Behaviour and limits
- **`REF_AREA` is required in `ilo_get_data`, up to 30 areas per call.** The ILO gateway times
out (HTTP 504) on unrestricted queries, so the server never issues one; for broad panels, split
the areas into batches and/or paginate by period (`start_period`/`end_period`). The error
message explains how.
- **One live REST call per data query.** Data is never cached — every `ilo_get_data` result is
fetched from ILOSTAT at request time. Dataflow structures (TTL 24 h) and codelists (TTL 7 days,
shared across dataflows) are cached.
- **`data_vintage`** is the dataflow's last-update date as published by the ILO (`LAST_UPDATE`
annotation, normalised to ISO).
- **`retrieved_at` is always the real instant of extraction from ILOSTAT**, preserved alongside
any cached value — never the build or response time. Cached responses say so
(`served_from_cache: true`).
- **The indicator catalogue is a local snapshot** (~1,210 dataflows), refreshed periodically; its
own `retrieved_at` is reported in the provenance of `ilo_search_indicators`, so its age is
always visible.
- **Every upstream call carries an identifiable User-Agent** (service URL + contact), so ILO
administrators can reach the operator.
- **Language: English; timezone: UTC** (ILO data is published in English).
### Provenance fields
- **`derived`** — `true` only for real transformation (aggregation, server-computed rate,
interpolation, harmonisation), always with a `derivation_note`; unit conversion and rounding
do not count. This server does not transform values, so `derived` is always `false`.
- **`notices`** — reproduces the values of `OBS_STATUS` (the SDMX status/disclaimer channel,
e.g. "Break in series"), verbatim and with counts. Technical per-observation attributes
(`DECIMALS` etc.) stay on the rows (`rows[].attributes`).
## Data license and attribution
- ILOSTAT data and metadata: **CC BY 4.0** (since 2023-05-03; license verified 2026-08-04).
- ILO attribution in every response (`citation` field):
`International Labour Organization, ILOSTAT, https://ilostat.ilo.org/data/, accessed <date>.`
- The ILO logo is not used. This service is not endorsed by the ILO.
## Self-hosting / development
Everything below is only needed to run your own instance — it is **not** required to use the
public server.
```bash
npm install
npm run typecheck && npm test # offline suite (parsers, key, tools, output contract, resources/prompts, in-memory catalogue, vocabulary, eval fixtures)
npm run dev # http://localhost:8787/mcp (Worker)
npm run build && npm start # stdio runtime (dist/cli.js)
# Catalogue seed (D1) — required before first use:
node scripts/seed-catalog.mjs # downloads via curl and generates scripts/seed-catalog.sql
npx wrangler d1 execute ilostat-catalog --local --file=scripts/seed-catalog.sql
npx wrangler d1 execute ilostat-catalog --remote --file=scripts/seed-catalog.sql
npm run deploy
node scripts/smoke-mcp.mjs # smoke test against production (initialize → 6 tools → search → fetch → errors)
npm run manifest:lhm # regenerate tools/resources/prompts in lhm.plugin.json from the real server
# (the seed also writes tests/fixtures/catalog-ids.txt — the versioned id list the tests check resources/prompts against)
```
Bindings (see `wrangler.jsonc`): KV `SDMX_CACHE`, D1 `CATALOG_DB`, Durable Object `USAGE`
(SQLite-backed usage counters), `CF_VERSION_METADATA`. Optional Bearer auth
(`wrangler secret put API_KEY`); token-bucket rate limit per IP.
Notes for operators:
- ILOSTAT returns JSON only when negotiated via the `Accept` header
(`application/vnd.sdmx.{structure,data}+json`); `?format=` is ignored and returns XML.
- The ILO gateway answers HTTP 500 (`languageTag1`) to the `Accept-Language: *` header that
Node's `fetch` (undici) sends by default; every upstream call therefore sets
`Accept-Language: en` explicitly (Cloudflare's runtime sends no such header, so the Worker was
never affected). It also expects an identifiable User-Agent.
- **Catalogue refresh** is manual (no cron): quarterly, or immediately if a dataflow that exists
upstream does not show up in search. Procedure: the three seed commands above. Data queries are
always live, so only the search catalogue can age — and its age is exposed in provenance.
## Evals
[`@sbissoli/mcp-evals`](https://www.npmjs.com/package/@sbissoli/mcp-evals): 24 fixtures in
`evals/fixtures/queries.ts`, validated offline in `npm test`. The run with a real model
(`npm run eval`) uses the Anthropic API and needs `ANTHROPIC_API_KEY` (without it, it exits with
instructions). Run of 2026-08-07: **top-1 100% (24/24)** — `evals/results/`.
**End-to-end**: 10 complex questions with a single verifiable answer in `evals/e2e/evaluation.xml`,
answers validated manually against production (`evals/e2e/validacao-respostas.md`). Run of
2026-08-07 (Sonnet): **9/10 exact string; 10/10 substantive** — `evals/results/2026-08-07-e2e.md`.
## Endpoints
| Route | Purpose |
|---|---|
| `/` | landing page (service identity + contact — public) |
| `/health` | liveness |
| `/status` | version, tool/resource/prompt counts and names, provenance contract version, current deploy (feeds the README badges) |
| `/metrics` | aggregated usage (MCP endpoint only; no IPs, no query content) |
| `/mcp` | MCP Streamable HTTP |
## Security
Snyk Agent Scan (2026-08-07): **passed** — report in
[`security/`](security/2026-08-07-snyk-agent-scan.md).
## License
Code: [MIT](LICENSE.md). Data: ILOSTAT, CC BY 4.0 (see "Data license and attribution" above).
## Privacy
Privacy policy of the hosted service: [PRIVACY.md](PRIVACY.md).
## Contact
Sidney da S. P. Bissoli — sbissoli76@gmail.com. This service is not endorsed by the ILO.
TDQS
A4.5/5.0
Scored across 6 tools
Disambiguation4/5
The data tools (ilo_search_indicators, ilo_get_indicator_metadata, ilo_list_dimension_values, ilo_get_data) are clearly separated by function. The generic search and fetch tools are distinct from each other, but search and ilo_search_indicators both search the same catalog, creating mild overlap; their descriptions do clarify that one serves document retrieval and the other serves dataflow discovery.
Naming Consistency3/5
The four ilo_* tools follow a consistent verb-based pattern with underscores, but the unprefixed generic tools search and fetch break the convention. The mixed prefixing is explainable but inconsistent across the set.
Tool Count5/5
Six tools is a well-scoped size for the server's purpose: discovery, metadata inspection, dimension lookup, and data retrieval each have a dedicated tool. No tool feels redundant or unnecessary for the stated domain.
Completeness5/5
The tool set covers the full workflow needed to query ILOSTAT data: search for indicators, inspect metadata, list valid dimension codes, and retrieve data values. The search/fetch pair also enables document-level research, so there are no obvious dead ends in the intended use cases.
Maintenance
ActivityActive
ResponsivenessNo issues