# MCP Server: INSEE French Statistics for AI Agents (`dltik/mcp-server-insee`) Actor MCP server: 6 tools for INSEE French statistics. Macro series, inflation, population, geo codes. HTTP-only, no API key. $0.01/call. - **URL**: https://apify.com/dltik/mcp-server-insee.md - **Developed by:** [dltik](https://apify.com/dltik) (community) - **Categories:** Developer tools, Business, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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 ## MCP Server: INSEE French Statistics for AI Agents > MCP server with **6 tools** to query official INSEE French statistics (macro time series, inflation, population, geo codes). HTTP-only, no API key. **$0.01 per tool-call**. ⭐ **Bookmark this actor** — Apify ranks actors by bookmarks, so bookmarking directly improves the visibility of MCP Server: INSEE on the Apify Store. ### What is the MCP Server: INSEE actor? **MCP Server: INSEE** is an Apify-hosted Model Context Protocol (MCP) server that gives AI agents structured access to official French statistics published by INSEE (Institut national de la statistique et des études économiques) and the French government geo registry. It exposes 6 callable tools over the standard MCP input contract (`mode`, `tool_name`, `arguments`), so any MCP-compatible client — Claude, Cursor, Continue, or a custom LangChain/LlamaIndex agent — can fetch French macroeconomic and demographic data without writing a single SDMX parser. The actor is backed by two free, official, no-authentication HTTP APIs: the INSEE BDM SDMX web service (`bdm.insee.fr/series/sdmx`) for macroeconomic time series and the French government geo API (`geo.api.gouv.fr`) for communes, departments and regions. There is no API key to manage, no OAuth flow, and no scraping — only stable government endpoints. That makes MCP Server: INSEE reliable for unattended agent workflows. ### Tools available - `search_indicators` — search INSEE statistics by keyword (inflation, unemployment, GDP, wages, population). Returns ready-to-use idbanks for headline indicators plus matching INSEE theme catalogs. - `get_series` — fetch any INSEE BDM macro time series by idbank. Returns series metadata + observations (period, value), with optional `since` and `limit`. - `population_by_area` — population for a French area by INSEE code: a commune (5 digits) returns its population; a department or region returns aggregated population and commune count. - `local_stats` — local profile for a commune by INSEE code: population, area in km², density, postal codes, intercommunality (EPCI), department and region. - `inflation_index` — French consumer price index (CPI): latest value, full history, and computed year-on-year inflation %. - `geo_codes` — resolve French place names to INSEE codes (and codes back to names) for communes, departments and regions. - `list_tools` — free discovery endpoint that returns the full tool catalog (no charge). ### Use cases - **French macro analysis for AI agents** — let an agent pull inflation, unemployment or GDP series from INSEE on demand and reason over the numbers. - **Market & territory research** — resolve a city or department to its INSEE code, then fetch its population and local statistics in the same agent run. - **Economic dashboards & RAG** — feed up-to-date French CPI and labour-market series into a retrieval pipeline or a reporting agent. - **Lead enrichment** — augment a French company or address dataset with the demographics of its commune, department or region. - **Automated monitoring** — schedule an agent to track the latest CPI print and year-on-year inflation each month. ### Input ```json { "mode": "call_tool", "tool_name": "inflation_index", "arguments": {} } ```` More examples: ```json { "mode": "call_tool", "tool_name": "search_indicators", "arguments": { "query": "unemployment" } } { "mode": "call_tool", "tool_name": "get_series", "arguments": { "idbank": "001688527", "since": "2015" } } { "mode": "call_tool", "tool_name": "geo_codes", "arguments": { "name": "Lyon" } } { "mode": "call_tool", "tool_name": "population_by_area", "arguments": { "code": "69123" } } ``` For discovery: `{"mode": "list_tools"}` (free, no charge). ### Pricing **PAY\_PER\_EVENT — $0.01 per tool-call** (= $10.00 per 1,000 calls). The `list_tools` discovery mode is free. INSEE and geo.api.gouv.fr are free official APIs, so MCP Server: INSEE has no upstream data cost. ### Author [dltik](https://apify.com/dltik) ### License MIT # Actor input Schema ## `mode` (type: `string`): list\_tools (free, returns the tool catalog) or call\_tool (charged $0.01 per call). ## `tool_name` (type: `string`): Tool to invoke. Use mode=list\_tools to discover the full catalog and per-tool arguments. ## `arguments` (type: `object`): Arguments object for the chosen tool. See list\_tools mode for the JSON schema of every tool. ## Actor input object example ```json { "mode": "call_tool", "tool_name": "search_indicators", "arguments": { "query": "inflation" } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "arguments": { "query": "inflation" } }; // Run the Actor and wait for it to finish const run = await client.actor("dltik/mcp-server-insee").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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": "inflation" } } # Run the Actor and wait for it to finish run = client.actor("dltik/mcp-server-insee").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": "inflation" } }' | apify call dltik/mcp-server-insee --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dltik/mcp-server-insee", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "MCP Server: INSEE French Statistics for AI Agents", "description": "MCP server: 6 tools for INSEE French statistics. Macro series, inflation, population, geo codes. HTTP-only, no API key. $0.01/call.", "version": "1.0", "x-build-id": "BBwJ2V9D0Wbkhay1e" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dltik~mcp-server-insee/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dltik-mcp-server-insee", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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-insee/runs": { "post": { "operationId": "runs-sync-dltik-mcp-server-insee", "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-insee/run-sync": { "post": { "operationId": "run-sync-dltik-mcp-server-insee", "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", "required": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "call_tool", "list_tools" ], "type": "string", "description": "list_tools (free, returns the tool catalog) or call_tool (charged $0.01 per call).", "default": "call_tool" }, "tool_name": { "title": "Tool name", "enum": [ "search_indicators", "get_series", "population_by_area", "local_stats", "inflation_index", "geo_codes" ], "type": "string", "description": "Tool to invoke. Use mode=list_tools to discover the full catalog and per-tool arguments.", "default": "search_indicators" }, "arguments": { "title": "Arguments", "type": "object", "description": "Arguments object for the chosen tool. See list_tools mode for the JSON schema of every tool." } } }, "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 } } } } } } } } } } ```