🇨🇭 Part of the Swiss Public Data MCP Portfolio

📊 swiss-statistics-mcp

MCP Server for Swiss Federal Statistical Office (BFS) data via STAT-TAB PxWeb API — 682 datasets across 21 themes, no authentication required

🇩🇪 Deutsche Version

Demo

Maturity

This server is Alpha (0.x) as per the PyPI classifier. Until 1.0:

• Tool names, input schemas, and output JSON keys MAY change between minor versions
• Pin cloud deployments to a specific git tag, not main
• Production use is acceptable for read-only Open Data scenarios; consider it experimental for anything user-facing

See CHANGELOG.md for breaking changes.

Overview

swiss-statistics-mcp provides AI-native access to the Swiss Federal Statistical Office (BFS) via the STAT-TAB PxWeb API, without authentication:

Anchor demo query: "How many students attended lower secondary schools in the canton of Zurich in 2024?" — real BFS figures, no hallucination.

Features

• 📊 15 tools: 8 across 21 statistical themes (682 datasets) + a 4-tool commune/historical reference layer + 2 construction/real-estate tools + a price-index tool
• 🔍 Full-text search across the entire BFS data catalogue
• 🎓 Convenience tools for education statistics and population data
• 🏗️ Construction statistics — new buildings/dwellings and building investment incl. the Arbeitsvorrat leading indicator
• 🏠 Price indices — construction price index (Baupreisindex, parsed series) and residential property price index (IMPI) via the BFS DAM/CKAN sources
• 🏔️ Cross-cantonal comparison for any table and variable
• 🔓 No API key required — all data under open licences
• ☁️ Dual transport — stdio (Claude Desktop) + Streamable HTTP (cloud)

Prerequisites

• Python 3.11+
• uv (recommended) or pip

Installation

# Clone the repository
git clone https://github.com/malkreide/swiss-statistics-mcp.git
cd swiss-statistics-mcp

# Install
pip install -e .
# or with uv:
uv pip install -e .

Or with uvx (no permanent installation):

uvx swiss-statistics-mcp

Quickstart

# stdio (for Claude Desktop)
python -m swiss_statistics_mcp.server

# Streamable HTTP, loopback only (default: host=127.0.0.1, port=8000)
python -m swiss_statistics_mcp.server --http --port 8000

# Streamable HTTP, all interfaces (only behind a reverse proxy with access control)
MCP_HOST=0.0.0.0 python -m swiss_statistics_mcp.server --http --port 8000
# or
python -m swiss_statistics_mcp.server --http --host 0.0.0.0 --port 8000

Try it immediately in Claude Desktop:

"How many teachers worked in the canton of Zurich in 2023?" "What is the population of canton Bern broken down by age?" "Compare the social assistance rate across all cantons for 2022."

Configuration

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "swiss-statistics": {
      "command": "python",
      "args": ["-m", "swiss_statistics_mcp.server"]
    }
  }
}

Or with uvx:

{
  "mcpServers": {
    "swiss-statistics": {
      "command": "uvx",
      "args": ["swiss-statistics-mcp"]
    }
  }
}

Config file locations:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Cursor / Windsurf / VS Code + Continue

The configuration syntax is identical to Claude Desktop. The file name depends on the client:

• Cursor: .cursor/mcp.json in the project folder, or ~/.cursor/mcp.json globally
• Windsurf: ~/.codeium/windsurf/mcp_config.json
• VS Code + Continue: .continue/config.json

Cloud Deployment (SSE for browser access)

For use via claude.ai in the browser (e.g. on managed workstations without local software).

⚠️ Security note — this server has no authentication. A public URL turns it into an open proxy to the BFS API on your deployment's IP. Any client with the URL can drive the tools, consume your platform quota, and attribute traffic to your IP. Two mitigations, in order of preference:

1. Put it behind access control — Render's «Private Service», Cloudflare Access, or a reverse proxy with Basic-Auth / IP allowlist in front of the container.
2. Accept it as a public open-data proxy — only acceptable because all data is BFS OGD (Public Open Data) and tools are read-only.

The server binds to 127.0.0.1 by default. To expose it on a container port you must explicitly set MCP_HOST=0.0.0.0 (e.g. as a Render env var) or pass --host 0.0.0.0. Do not do this without one of the mitigations above.

Render.com:

1. Push/fork the repository to GitHub
2. On render.com: New Web Service → connect GitHub repo
3. Set environment variable: MCP_HOST=0.0.0.0
4. Set start command: python -m swiss_statistics_mcp.server --http --port 8000
5. In claude.ai under Settings → MCP Servers, add: https://your-app.onrender.com/sse

💡 "stdio for the developer laptop, SSE for the browser."

Output Schema

Since v0.2.0, every tool returns a typed Pydantic model rather than a JSON string. FastMCP serializes these as structured content so MCP clients can read fields directly.

# Old (pre-0.2.0)
result = await bfs_get_data(...)        # str
data = json.loads(result)               # dict
print(data["rows_total"])

# New (>= 0.2.0)
result = await bfs_get_data(...)        # DataTableResult
print(result.rows_total)                # 1000
print(result.truncated)                 # True

Every result carries error: str | None and hint: str | None at the top level — result.error is None means success. Data-returning tools (bfs_get_data, bfs_education_stats, bfs_population, bfs_compare_cantons) additionally expose truncated: bool, rows_total: int, and rows_returned: int for machine-readable cap detection.

Reference-layer results additionally carry source (attribution) and provenance (live_api | cached); SearchHistoricalSeriesResult also carries licence_note with the mandatory HSSO NonCommercial notice. The construction and price-index results carry source + provenance on the same envelope pattern.

Available Tools