siret-mcp

MCP server for French company registry data. Ask about any French company by name, SIREN or SIRET and get back clean JSON — with every registry code already translated into plain French.

The official registry hands you "nature_juridique": "5710", "activite_principale": "62.01Z", "tranche_effectif_salarie": "12". An LLM handed that either guesses or hallucinates. This server hands it "Société par actions simplifiée (SAS)", "Programmation informatique", "20 à 49 salariés" — with the original code alongside so the answer stays checkable.

No API key. No account.

Install

Claude Code

claude mcp add siret -- npx -y siret-mcp

Cursor — ~/.cursor/mcp.json:

{
  "mcpServers": {
    "siret": {
      "command": "npx",
      "args": ["-y", "siret-mcp"]
    }
  }
}

Claude Desktop — claude_desktop_config.json, same block as Cursor.

Restart the client. You should see four tools.

Try these

• "Is SIREN 552100554 still active, and what legal form is it?"
• "Find software companies in Loire-Atlantique with more than 20 employees."
• "What does NAF code 43.22A mean?"
• "Here's a SIRET from an invoice: 55210055400015 — verify it and tell me the registered address."
• "List every establishment for this SIREN and flag which is the head office."

Tools

| Tool | What it does | |---|---| | search_companies | Fuzzy search by name, trade name, acronym or officer. Filters: département, postal code, NAF code, active-only. Returns compact summaries. | | get_company | Full profile from a SIREN (9 digits) or SIRET (14). Validates the Luhn checksum before spending a request. | | list_establishments | All known sites for a SIREN, head office first, with a complete flag so you know if the list is exhaustive. | | explain_code | Decodes NAF / legal form / workforce bracket / status. Offline, no API call. |

Plus a siret://health resource exposing cache stats.

Design decisions worth knowing

Codes always travel with labels. Every coded field returns {code, label, exact, source}. exact: false means the label came from a fallback (a NAF section rather than the precise class), so a model can hedge appropriately instead of stating a guess as fact.

Absent fields are named, not nulled. Each profile carries a missing array. Given "workforce": null, a model will often invent a headcount. Given "missing": ["workforce"], it says the registry doesn't hold it.

Errors are returned, not thrown. Failures come back as JSON with a hint, so the model can retry or reformulate rather than dying mid-conversation.

Caching is the product, not an optimisation. The upstream rate-limits bursts. A 6h TTL cache (SIRENE_CACHE_TTL_MS) is what makes this usable in an agent loop that asks about the same company nine times.

Four tools, not twelve. Every extra tool costs the calling model context and adds a way for it to pick wrong.

Limitations

• Code tables are partial. Full NAF rev. 2 is ~732 codes and catégorie juridique ~300; this ships the common ones plus a fallback that flags itself as approximate (exact: false).
• Backed by recherche-entreprises.api.gouv.fr, not INSEE Sirene v3. No API key means this runs immediately. The trade-off is fewer fields and no exhaustive establishment listing.
• Cache is in-process. Fine for a local stdio server, wrong for a hosted multi-tenant one.

Data & attribution

Data comes from the API Recherche d'Entreprises (DINUM), derived from INSEE SIRENE, under Licence Ouverte. Officer records may include a birth year. You are responsible for using the data lawfully.

Development

git clone https://github.com/tbellicha/siret-mcp.git
cd siret-mcp
npm install
npm run build
npm test                 # node:test, offline
npm run test:live        # hits the real API
npm run typecheck

Licence

MIT © 2026 tbellicha