# Statistics Finland (StatFin) MCP (`nectia/statfin-mcp`) Actor Search Statistics Finland's StatFin database (3,000+ tables), inspect table variables, and query clean flattened statistics on population, economy, labour and regions. - **URL**: https://apify.com/nectia/statfin-mcp.md - **Developed by:** [Anton Aouat](https://apify.com/nectia) (community) - **Categories:** MCP servers - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 search statistical tables This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Statistics Finland (StatFin) MCP An MCP server that gives an AI agent clean, queryable access to **Statistics Finland's StatFin database** — 3,000+ official statistical tables on population, economy, labour, housing, prices, and regions. The server does the hard part: it turns StatFin's dimensional **json-stat2** cubes into flat, labelled rows an agent can read directly. Data © Statistics Finland, StatFin database, licensed **CC BY 4.0**. ### Tools | Tool | What it does | |------|--------------| | `search_tables` | Find tables by keyword. Returns a ready-to-use `table` path (e.g. `vaerak/11rb.px`), title, and publication date. | | `get_table_metadata` | List a table's variables (dimensions) and their value codes + labels — the codes you need to query. Flags `time` and `eliminable` variables. | | `get_data` | Query a table and get **flattened, labelled rows** (json-stat2 decoded for you). Oversize selections (>100,000 cells) are refused with a hint to narrow. | Agents chain them: **search → metadata (learn the codes) → data**. Every tool takes an optional `database` (default `StatFin`; also `Kuntien_avainluvut`, `Postinumeroalueittainen_avoin_tieto`, `StatFin_Passiivi`) and `language` (`en`/`fi`/`sv`). ### Example > "What was Finland's population at the end of 2024 and 2025, by sex?" 1. `search_tables({ query: "population" })` → finds `vaerak/11rb.px`. 2. `get_table_metadata({ table: "vaerak/11rb.px" })` → variables `timeperiod_y` (Year), `sukupuoli_9_20180101` (Sex, values `SSS`/`1`/`2`), `contentscode`. 3. `get_data({ table: "vaerak/11rb.px", selections: { timeperiod_y: ["2024","2025"], sukupuoli_9_20180101: ["SSS","1","2"], contentscode: ["vaerak-vaesto"] } })` → ```json { "label": "Population 31.12. by Year, Sex and Information", "source": "Statistics Finland, population structure", "rowCount": 6, "dimensions": [ { "code": "timeperiod_y", "label": "Year", "size": 2 }, { "code": "sukupuoli_9_20180101", "label": "Sex", "size": 3 }, { "code": "contentscode", "label": "Information", "size": 1 } ], "rows": [ { "Year": "2024", "Sex": "Total", "Information": "Population 31 Dec", "value": 5635971 }, { "Year": "2024", "Sex": "Males", "Information": "Population 31 Dec", "value": 2790772 }, { "Year": "2024", "Sex": "Females", "Information": "Population 31 Dec", "value": 2845199 } ] } ```` ### Local use (stdio) ```bash npm install npm run build ``` Claude Desktop config: ```json { "mcpServers": { "statfin": { "command": "node", "args": ["/absolute/path/to/servers/statfin/dist/server.js"] } } } ``` No API key is required — StatFin is open data. ### Development ```bash npm run typecheck # tsc --noEmit npm test # vitest — unit tests vs recorded real fixtures (offline) npm run smoke # live: boots the server, real MCP handshake + real StatFin calls npm run build # produces dist/ ``` Windows note: this repo lives under a path with spaces, which breaks npm's `.bin` shims. Run tools directly (`node ./node_modules/vitest/vitest.mjs run`, `node ./node_modules/typescript/bin/tsc`) or run the npm scripts from PowerShell. ### Design - `src/statfin/client.ts` — HTTP only (GET search + metadata, POST data). Throttle (~1 req/1.5 s, under the 40 req/60 s limit) + short TTL cache. Typed `StatFinApiError`. - `src/statfin/normalize.ts` — the product: tidy search hits, flatten metadata, and **decode json-stat2 cubes into flat rows**. - `src/statfin/query.ts` — build a PxWeb query from a simple `{ code: values[] }` selection; validate codes, expand `*`, aggregate eliminable variables, and guard the cell limit before hitting the network. - `src/tools.ts` — the 3 MCP tools + Pay-Per-Event billing hook. - `src/server.ts` (stdio) / `src/standby.ts` (Apify HTTP) — both call one factory. # Actor input Schema ## Actor input object example ```json {} ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("nectia/statfin-mcp").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 = {} # Run the Actor and wait for it to finish run = client.actor("nectia/statfin-mcp").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 '{}' | apify call nectia/statfin-mcp --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nectia/statfin-mcp", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Statistics Finland (StatFin) MCP", "description": "Search Statistics Finland's StatFin database (3,000+ tables), inspect table variables, and query clean flattened statistics on population, economy, labour and regions.", "version": "0.1", "x-build-id": "9Or0lOfgmej8jCnQz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nectia~statfin-mcp/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nectia-statfin-mcp", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nectia~statfin-mcp/runs": { "post": { "operationId": "runs-sync-nectia-statfin-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nectia~statfin-mcp/run-sync": { "post": { "operationId": "run-sync-nectia-statfin-mcp", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": {} }, "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 } } } } } } } } } } ```