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statfin-mcp

mcp
Guvenlik Denetimi
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  • network request — Outbound network request in scripts/generate-demos.ts
  • process.env — Environment variable access in src/config.ts
  • network request — Outbound network request in src/services/pxweb-client.ts
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SUMMARY

Production-grade Model Context Protocol (MCP) server for Statistics Finland's StatFin database. Enables AI assistants like Claude to browse, search, and query Finnish statistical data.

README.md

Tilastokeskus StatFin MCP Server

Production-grade Model Context Protocol (MCP) server for Statistics Finland's StatFin database. Enables AI assistants like Claude to browse, search, and query Finnish statistical data.

Purpose

This project makes Finland's official statistics accessible to AI assistants through the Model Context Protocol (MCP). It bridges the gap between natural language queries and the structured PxWeb API.

Why This Exists

The Problem: Statistics Finland maintains one of the world's most comprehensive national statistics databases with 4,500+ tables covering population, employment, housing, economy, and more. However, the PxWeb API requires:

  • Knowledge of table IDs, variable codes, and value codes
  • Understanding of the hierarchical data structure
  • Correct query formatting with specific filter types
  • Awareness of API rate limits and response sizes

This makes it difficult for users to answer simple questions like "What is Helsinki's population?" without significant technical knowledge.

The Solution: This MCP server provides 7 tools that allow AI assistants to:

  1. Discover relevant tables through natural language search
  2. Explore the database structure and available variables
  3. Query specific data with proper filtering and pagination
  4. Handle rate limiting, caching, and error recovery automatically

Use Cases

  • Journalists asking "How has unemployment changed since COVID?"
  • Researchers comparing regional population trends
  • Analysts tracking housing price divergence between Helsinki and other cities
  • Citizens curious about birth rates, migration, or energy consumption
  • Developers building applications that need Finnish statistical data

Design Philosophy

  • LLM-first: Tool descriptions, parameter hints, and output schemas are optimized for AI consumption
  • Guided workflow: Each tool guides the LLM to the next logical step
  • Fail-safe: Query size estimation prevents expensive API calls; rate limiting protects against quota exhaustion
  • Cache-smart: Historical data is cached until StatFin updates it, minimizing redundant API calls

Features

  • 7 MCP Tools for comprehensive data access
  • 149 subject areas covering population, employment, education, economy, environment, and more
  • ~4,500+ statistical tables with decades of historical data
  • Smart caching with timestamp validation - historical data stays cached until updated
  • Rate limiting (8 req/min per instance) to respect API limits
  • Multi-language support (Finnish, English, Swedish)

Documentation

  • ARCHITECTURE.md - Technical architecture and design decisions
  • BLOGS.md - Blog post ideas and data stories (English)
  • BLOGIT.md - Blog post ideas and data stories (Finnish)

Data Source & License

Statistics Finland StatFin Database

  • Official statistics of Finland
  • Data updated regularly (varies by table)
  • Free to use, no API key required

Data License: Statistics Finland data is licensed under CC BY 4.0. When using the data, provide attribution: "Source: Statistics Finland"

Installation

Local Installation

Clone and build the server to run on your machine.

git clone https://github.com/your-org/statfin-mcp.git
cd statfin-mcp
npm install
npm run build
Claude Desktop

Add to your config file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json
{
  "mcpServers": {
    "statfin": {
      "command": "node",
      "args": ["/absolute/path/to/statfin-mcp/dist/server.js"]
    }
  }
}
Claude Code 
claude mcp add statfin node /absolute/path/to/statfin-mcp/dist/server.js

Verify with:

claude mcp list
OpenAI Codex / ChatGPT

Add to ~/.codex/config.toml:

[mcp.statfin]
command = "node"
args = ["/absolute/path/to/statfin-mcp/dist/server.js"]
transport = "stdio"
Cursor

In Cursor settings, add MCP server with command:

  • Name: statfin
  • Command: node
  • Args: /absolute/path/to/statfin-mcp/dist/server.js

Remote Server

Connect to a hosted instance without local installation.

Claude Code 
claude mcp add --transport http statfin https://your-server.example.com/mcp

Verify with:

claude mcp list
Claude Desktop

Add to your config file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json
{
  "mcpServers": {
    "statfin": {
      "type": "http",
      "url": "https://your-server.example.com/mcp"
    }
  }
}
OpenAI Codex / ChatGPT

Add to ~/.codex/config.toml:

[mcp.statfin]
url = "https://your-server.example.com/mcp"
transport = "http"
Cursor

In Cursor settings, add MCP server:

  • Name: statfin
  • URL: https://your-server.example.com/mcp
  • Transport: HTTP

Running the Server

# Development mode (stdio transport, watch mode)
npm run dev

# Production HTTP server
npm start

# With custom port
PORT=8080 npm start

# Docker
docker-compose up --build

MCP Tools

Tool Description
search_statistics Search for tables by keyword (primary discovery)
list_subject_areas Browse all 149 topic areas
list_tables List tables in a subject area
get_table_metadata Get table structure and variables
get_variable_values Get all codes for a variable (regions, years)
query_table Execute data queries
get_api_status Check server health and rate limits

Example Workflows

⚠️ Variable codes changed in the 8 June 2026 PxWeb migration. They are now version-stamped and table-specific (e.g. alue_23_20260101, timeperiod_y), so the variable codes shown in the advanced examples below are illustrative — always call get_table_metadata to get the exact codes for your table. Table IDs are now short (11re.px, not statfin_vaerak_pxt_11re.px). Value codes (SSS, KU091, MK01…) are unchanged.

Basic: Helsinki Population Trend

// Codes below are the current codes for table 11re.px (verified).
query_table({
  tableId: "11re.px",
  selections: [
    { variable: "alue_23_20260101", filter: "item", values: ["KU091"] },     // Helsinki
    { variable: "ikaryhma_10_20180101", filter: "item", values: ["SSS"] },    // All ages
    { variable: "sukupuoli_9_20180101", filter: "item", values: ["SSS"] },    // Total
    { variable: "timeperiod_y", filter: "top", top: 10 },                     // Last 10 years
    { variable: "contentscode", filter: "item", values: ["vaerak-vaesto"] }   // Population
  ]
})
// Returns: Helsinki population trend over the last 10 years

Advanced Example Queries

The variable codes in these examples are conceptual (pre-migration names shown for readability). Resolve the real, current codes for each table via get_table_metadata before querying — only the short table IDs and the value codes are guaranteed current.

Education: University Student Employment by Field

Which fields of study have the highest employment rates for students?

// Table: Student employment by education level and field
query_table({
  tableId: "13g2.px",
  selections: [
    { variable: "Koulutusaste", filter: "item", values: ["7"] },  // University level
    { variable: "Sukupuoli", filter: "item", values: ["SSS"] },   // All genders
    { variable: "Maakunta", filter: "item", values: ["SSS"] },    // Whole country
    { variable: "Vuosi", filter: "top", top: 3 }                  // Last 3 years
  ]
})
// Analyze: Compare IT vs humanities employment rates

Housing: Rent Price Trends by Postal Code

Track rental market changes in Helsinki city center vs suburbs

// Table: Free-market rental prices by postal code, quarterly
query_table({
  tableId: "13eb.px",
  selections: [
    { variable: "Postinumero", filter: "item", values: [
      "00100",  // Helsinki center (Kruununhaka)
      "00500",  // Sörnäinen
      "02100",  // Espoo Tapiola
      "01300"   // Vantaa Tikkurila
    ]},
    { variable: "Huoneluku", filter: "item", values: ["02"] },  // 2-room apartments
    { variable: "Vuosineljännes", filter: "top", top: 20 }      // 5 years quarterly
  ]
})
// Analyze: Which areas are gentrifying fastest?

Migration: International Migration Flows

Analyze emigration vs immigration patterns over decades

// Table: Migration by month and type
query_table({
  tableId: "119z.px",
  selections: [
    { variable: "Sukupuoli", filter: "item", values: ["SSS"] },
    { variable: "Tapahtumakuukausi", filter: "item", values: ["SSS"] },  // Annual totals
    { variable: "Tiedot", filter: "item", values: [
      "vm41",  // Immigration
      "vm42",  // Emigration
      "vm43"   // Net migration
    ]},
    { variable: "Vuosi", filter: "top", top: 30 }  // 30-year trend
  ]
})
// Analyze: How has Finland's migration balance changed since 1990?

Crime: Monthly Crime Statistics Trends

Track reported crimes by category with seasonal patterns

// Table: Reported crimes by month (preliminary data)
query_table({
  tableId: "13jt.px",
  selections: [
    { variable: "Rikosryhmä ja teonkuvauksen tarkenne", filter: "item", values: [
      "101T603",     // All crimes total
      "101T504X406", // Violent crimes
      "101T161"      // Property crimes
    ]},
    { variable: "Tiedot", filter: "item", values: ["rikokset_lkm"] },
    { variable: "Kuukausi", filter: "top", top: 60 }  // 5 years monthly
  ]
})
// Analyze: Seasonal crime patterns, COVID impact on crime rates

Electricity: Power Generation Mix Evolution

How has Finland's electricity production evolved toward renewables?

// Table: Electricity supply and production by source
query_table({
  tableId: "11sr.px",
  selections: [
    { variable: "Tiedot", filter: "item", values: [
      "sahkon_tuot",       // Total production
      "vesivoima",         // Hydropower
      "tuulivoima",        // Wind power
      "ydinvoima",         // Nuclear
      "fossiiliset"        // Fossil fuels
    ]},
    { variable: "Vuosi", filter: "top", top: 25 }  // 25-year transition
  ]
})
// Analyze: Nuclear vs wind growth, fossil phase-out trajectory

Traffic: Road Accident Hotspots by Municipality

Which municipalities have the highest traffic accident rates?

// Table: Traffic accidents with injuries by area and road type
query_table({
  tableId: "12qh.px",
  selections: [
    { variable: "Alue", filter: "item", values: [
      "SSS",    // Whole country (for comparison)
      "KU091",  // Helsinki
      "KU092",  // Vantaa
      "KU049",  // Espoo
      "KU837",  // Tampere
      "KU853"   // Turku
    ]},
    { variable: "Tielaji", filter: "item", values: ["SSS"] },
    { variable: "Osallinen", filter: "item", values: ["SSS"] },
    { variable: "Tiedot", filter: "item", values: [
      "konn",     // Total accidents
      "kuolonn",  // Fatal accidents
      "loukonn"   // Injury accidents
    ]},
    { variable: "Vuosi", filter: "top", top: 5 }
  ]
})
// Analyze: Per-capita accident rates, pedestrian vs vehicle involvement

Housing: Household Size and Building Type Changes

How are Finnish living patterns changing over time?

// Table: Households by size and building type
query_table({
  tableId: "116a.px",
  selections: [
    { variable: "Talotyyppi", filter: "item", values: [
      "1",  // Detached houses
      "2",  // Attached houses
      "3",  // Apartment buildings
    ]},
    { variable: "Asuntokunnan koko", filter: "item", values: [
      "1",  // 1-person households
      "2",  // 2-person
      "3",  // 3-person
      "4"   // 4+ person
    ]},
    { variable: "Vuosi", filter: "top", top: 40 }  // Since 1985
  ]
})
// Analyze: Rise of single-person households, apartment living trends

Electric Vehicles: Adoption Rate by Vehicle Type

Track the EV transition in Finland's vehicle fleet

// Table: New vehicle registrations by fuel type
query_table({
  tableId: "11ck.px",
  selections: [
    { variable: "Ajoneuvoluokka", filter: "item", values: ["01"] },  // Passenger cars
    { variable: "Käyttövoima", filter: "item", values: [
      "00",  // Total
      "01",  // Petrol
      "02",  // Diesel
      "04",  // Electric
      "05",  // Plug-in hybrid
    ]},
    { variable: "Vuosi", filter: "top", top: 15 }
  ]
})
// Analyze: EV market share growth, diesel decline post-2015

Multi-Step Analysis Patterns

Cross-Domain Analysis: Education → Employment → Income

1. Find education completion rates by field
2. Get employment statistics for recent graduates
3. Query income data by education level
4. Compare: Which fields offer best ROI?

Time-Series with Regional Breakdown

1. Get national trend (region variable, value "SSS")
2. Compare major cities (KU091, KU837, KU853)
3. Identify regional divergence patterns
4. Correlate with local economic indicators

Demographic Shift Analysis

1. Query population by age groups (1990-2024)
2. Get migration data for same period
3. Query birth/death rates
4. Model: Aging population impact on workforce

Common Region Codes

Code Description
SSS Whole country (KOKO MAA)
MK01-MK19 Regions (maakunta)
KU091 Helsinki
KU092 Vantaa
KU049 Espoo

Development

# Type checking
npm run typecheck

# Run unit tests
npm run test:run

# Run tests with coverage
npm run test:coverage

# Run integration tests (requires API access)
npm run test:integration

# Fetch fresh test fixtures
npm run test:fixtures:fetch

Environment Variables

Variable Default Description
PORT 8080 HTTP server port
MCP_TRANSPORT stdio Set to "http" for HTTP transport
API_TOKEN - Optional authentication token
LOG_LEVEL info Logging level (debug, info, warn, error)

API Rate Limits

  • StatFin API: 30 requests/minute total
  • This server: 8 requests/minute per instance
  • Designed for up to 3 concurrent Cloud Run instances

License

This project (code) is licensed under MIT. The statistical data accessed through this server is provided by Statistics Finland under CC BY 4.0.

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