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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.
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.
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.
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:
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:
Data License: Statistics Finland data is licensed under CC BY 4.0. When using the data, provide attribution: "Source: Statistics Finland"
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
Add to your config file:
~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.json{
"mcpServers": {
"statfin": {
"command": "node",
"args": ["/absolute/path/to/statfin-mcp/dist/server.js"]
}
}
}
claude mcp add statfin node /absolute/path/to/statfin-mcp/dist/server.js
Verify with:
claude mcp list
Add to ~/.codex/config.toml:
[mcp.statfin]
command = "node"
args = ["/absolute/path/to/statfin-mcp/dist/server.js"]
transport = "stdio"
In Cursor settings, add MCP server with command:
statfinnode/absolute/path/to/statfin-mcp/dist/server.jsConnect to a hosted instance without local installation.
claude mcp add --transport http statfin https://your-server.example.com/mcp
Verify with:
claude mcp list
Add to your config file:
~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.json{
"mcpServers": {
"statfin": {
"type": "http",
"url": "https://your-server.example.com/mcp"
}
}
}
Add to ~/.codex/config.toml:
[mcp.statfin]
url = "https://your-server.example.com/mcp"
transport = "http"
In Cursor settings, add MCP server:
statfinhttps://your-server.example.com/mcp# 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
⚠️ 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 callget_table_metadatato get the exact codes for your table. Table IDs are now short (11re.px, notstatfin_vaerak_pxt_11re.px). Value codes (SSS,KU091,MK01…) are unchanged.
// 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
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.
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
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?
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?
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
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
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
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
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
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?
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
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
# 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
This project (code) is licensed under MIT. The statistical data accessed through this server is provided by Statistics Finland under CC BY 4.0.
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.
เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Statfin ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-10-28 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.