# MCP Server: French Companies (Pappers + Sirene) (`dltik/mcp-server-pappers`) Actor Apify-hosted MCP server exposing 5 tools for AI agents: search/get/dirigeants/by-NAF/by-city across 26M+ French companies. Backed by official gov API, no key required, $0.01 per tool call. - **URL**: https://apify.com/dltik/mcp-server-pappers.md - **Developed by:** [dltik](https://apify.com/dltik) (community) - **Categories:** AI, Business, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing $10.00 / 1,000 tool calls This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Pappers MCP Server — 26M French Companies (SIREN/SIRET) for AI Agents > Apify-hosted **MCP server** giving AI agents (Claude, Cursor, Continue) instant access to 26M+ French companies via the official Sirene API. Search by name, SIREN, SIRET, NAF code, city, or executive (dirigeant). No API key, no proxy, free official government data. **$0.01 per tool call**. ⭐ **Bookmark this Pappers MCP Server** — Apify ranks actors by bookmarks, so it directly helps the visibility of this server on the Apify Store. ### What is the Pappers MCP Server? The **Pappers MCP Server** is an Apify actor implementing the [Model Context Protocol](https://modelcontextprotocol.io/) for the [Pappers / Sirene database](https://www.pappers.fr/) — France's official company registry. It exposes 5 tools your AI agent can call to search, fetch, and enrich French company records (Pappers data + Sirene data combined). Backed by the free official endpoint at `recherche-entreprises.api.gouv.fr` — no API key, no rate limit headaches, no proxy. Use it standalone via the Apify API, or wire it into Claude Desktop / Cursor / Continue.dev as an MCP server to give your agent live B2B intelligence on French companies. ### Use cases - **B2B prospecting agents** — let your AI agent build SIREN-keyed lead lists by NAF code, city, or executive name. - **KYC / compliance copilots** — verify SIRET, get registered address, dirigeants, legal form, in one tool call. - **CRM enrichment bots** — auto-fill missing fields (NAF, GPS, headcount band) on existing France records. - **Market sizing** — count active companies by NAF, by department, by employee band. - **Investigative journalism / OSINT** — find all companies of a given executive across France. ### Tools available | Tool | What it returns | |---|---| | `search_companies(query, limit)` | Up to 25 companies matching free-text query | | `get_company(siren_or_siret)` | One full company record (SIREN-resolved) | | `find_dirigeants(name, limit)` | Companies linked to a person's name | | `companies_by_naf(naf_code, limit)` | Companies by activity code (e.g. `62.01Z`) | | `companies_by_city(city, limit)` | Companies by `libelle_commune` | | `list_tools` | Free — schema discovery | ### Input ```json { "mode": "call_tool", "tool_name": "search_companies", "arguments": { "query": "Decathlon", "limit": 5 } } ```` Set `mode: "list_tools"` to get the full input schema for every tool — that call is free. ### Output ```json { "siren": "306138900", "siret": "30613890051571", "name": "DECATHLON FRANCE", "naf_code": "47.64Z", "naf_label": "Commerce de détail d'articles de sport", "headquarters": { "city": "VILLENEUVE-D'ASCQ", "postal_code": "59650" }, "dirigeants": [{ "first_name": "Barbara", "last_name": "MARTIN COIA", "role": "Présidente" }], "pappers_url": "https://www.pappers.fr/entreprise/306138900" } ``` ### Pricing **PAY\_PER\_EVENT — $0.01 per tool call** (= $10 per 1,000). Free actor starts. The `list_tools` discovery call is free. No charge if a tool returns an error. ### FAQ **Pappers API or Sirene — which one?** Both. The actor uses `recherche-entreprises.api.gouv.fr`, which fuses Sirene data with INSEE data, executives data (dirigeants), and BODACC events. You get richer records than the raw Sirene API. **Why MCP instead of a regular scraper?** MCP lets an AI agent *decide* which tool to call dynamically. For deterministic batch enrichment, see the standalone [Pappers / Sirene Scraper](https://apify.com/dltik/pappers-sirene-scraper). **Does it work in Claude Desktop?** Yes — wire the Apify actor URL as an MCP server and your AI agent gets the 5 tools instantly. *** ⭐ **Found this useful? Bookmark this Pappers MCP Server** — it's the strongest signal for Apify Store ranking. #### Related actors - [Pappers / Sirene Scraper](https://apify.com/dltik/pappers-sirene-scraper) — bulk batch version, same data, no MCP wrapper - [BODACC MCP Server](https://apify.com/dltik/mcp-server-bodacc) — French court announcements (M\&A, distress, new businesses) - [SEC EDGAR MCP Server](https://apify.com/dltik/mcp-server-sec-edgar) — same pattern for US public companies License: MIT · Author: [dltik](https://apify.com/dltik) # Actor input Schema ## `mode` (type: `string`): list\_tools (free, returns tool catalog) or call\_tool (charged $0.01 per call). ## `tool_name` (type: `string`): Tool to call. Use mode=list\_tools to discover available tools. ## `arguments` (type: `object`): Arguments for the chosen tool. See list\_tools mode for full schema. ## Actor input object example ```json { "mode": "call_tool", "tool_name": "search_companies", "arguments": { "query": "Decathlon", "limit": 5 } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "arguments": { "query": "Decathlon", "limit": 5 } }; // Run the Actor and wait for it to finish const run = await client.actor("dltik/mcp-server-pappers").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "arguments": { "query": "Decathlon", "limit": 5, } } # Run the Actor and wait for it to finish run = client.actor("dltik/mcp-server-pappers").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "arguments": { "query": "Decathlon", "limit": 5 } }' | apify call dltik/mcp-server-pappers --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dltik/mcp-server-pappers", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "MCP Server: French Companies (Pappers + Sirene)", "description": "Apify-hosted MCP server exposing 5 tools for AI agents: search/get/dirigeants/by-NAF/by-city across 26M+ French companies. Backed by official gov API, no key required, $0.01 per tool call.", "version": "1.0", "x-build-id": "DwTlN3KRayFPXVE2O" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dltik~mcp-server-pappers/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dltik-mcp-server-pappers", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dltik~mcp-server-pappers/runs": { "post": { "operationId": "runs-sync-dltik-mcp-server-pappers", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dltik~mcp-server-pappers/run-sync": { "post": { "operationId": "run-sync-dltik-mcp-server-pappers", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "mode": { "title": "Mode", "enum": [ "call_tool", "list_tools" ], "type": "string", "description": "list_tools (free, returns tool catalog) or call_tool (charged $0.01 per call).", "default": "call_tool" }, "tool_name": { "title": "Tool name", "enum": [ "search_companies", "get_company", "find_dirigeants", "companies_by_naf", "companies_by_city" ], "type": "string", "description": "Tool to call. Use mode=list_tools to discover available tools.", "default": "search_companies" }, "arguments": { "title": "Arguments", "type": "object", "description": "Arguments for the chosen tool. See list_tools mode for full schema." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```