🇨🇭 Part of the Swiss Public Data MCP Portfolio

wsl-envidat-mcp 🌲❄️⛰️

MCP server connecting AI models to Swiss environmental research data from WSL via EnviDat — forest, snow, avalanches, natural hazards and biodiversity, no API key required.

🇩🇪 Deutsche Version

Phase

This server is in Phase 1: Read-only Wrapper.

Phase-2 ideas (caching layer, semantic aggregation tool combining forest + snow + hazard data into a "Lage-Übersicht"): tracked under docs/.

Overview

The WSL (Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft / Swiss Federal Research Institute for Forest, Snow and Landscape) is one of Europe's leading environmental research institutes. Its open data platform EnviDat provides access to 1,000+ research datasets, time series of up to 130 years, and data from 6,000+ monitoring stations.

This MCP server exposes the EnviDat CKAN API as 10 tools and 2 resources, enabling AI assistants to search, filter and retrieve WSL research data by keyword, domain, or geographic bounding box — all without an API key.

Anchor demo query: "How was air quality and forest health around Schulhaus Leutschenbach in Zurich — and what does the WSL say about the current forest condition in the canton?"

Demo

Features

• 10 tools covering full-text search, domain-specific queries, spatial search, and curated thematic tools (avalanche, forest, natural hazards)
• 2 MCP resources for organizations and research domains
• 5 research domains: Forest · Biodiversity · Natural Hazards · Snow & Ice · Landscape
• 815+ datasets, time series since 1890, data from the SLF avalanche research institute
• No API key required — all data publicly accessible via open licenses
• Dual transport: stdio (Claude Desktop / local) + Streamable HTTP (cloud deployment)
• Model-agnostic: works with Claude, GPT-4, and any MCP-compatible client

Prerequisites

• Python 3.11+
• pip or uv / uvx
• Internet connection (live API calls to envidat.ch)

Installation

# Recommended: uvx (no installation needed)
uvx wsl-envidat-mcp

# Or with pip
pip install wsl-envidat-mcp

# Development
git clone https://github.com/malkreide/wsl-envidat-mcp.git
cd wsl-envidat-mcp
pip install -e ".[dev]"

Quickstart

Claude Desktop

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

{
  "mcpServers": {
    "wsl-envidat": {
      "command": "uvx",
      "args": ["wsl-envidat-mcp"]
    }
  }
}

Restart Claude Desktop, then ask:

• "What WSL datasets exist on fatal avalanche accidents in Switzerland?"
• "Show me forest inventory data from the LFI for the canton of Zurich."
• "Which natural hazard research data does the SLF publish on EnviDat?"
• "Are there WSL datasets on drought conditions in summer 2022?"
• "What biodiversity data is available for alpine ecosystems?"

Configuration

No API key required. Optional environment variables:

Cloud Deployment (Streamable HTTP)

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

# Local: keep MCP_HOST at its default 127.0.0.1
MCP_TRANSPORT=streamable-http PORT=8000 python -m wsl_envidat_mcp.server

# Container: bind to all interfaces inside the container only
MCP_TRANSPORT=streamable-http MCP_HOST=0.0.0.0 PORT=8000 python -m wsl_envidat_mcp.server

💡 "stdio for the developer laptop, streamable-http for the browser."

⚠️ Multi-Replica Cloud Deployments: Session state lives in the server. Run a single replica or enable sticky sessions (Railway/Render setting, or sessionAffinity: ClientIP on Kubernetes Services).

⚠️ Multi-Tenant / Unauthenticated Streamable HTTP: This server has no auth layer (auth_model: none). Streamable HTTP without a reverse-proxy + OAuth/API-Gateway is intended only for single-user deployments (e.g. one user's claude.ai browser session). For multi-tenant use, front the server with an authenticating gateway.

Container image (recommended for cloud)

A hardened multi-stage image is published to GitHub Container Registry on every main push and semver tag. Runs as non-root (uid=1000), no build tools in the runtime layer, multi-arch (linux/amd64 + linux/arm64).

The publishing workflow is .github/workflows/container.yml — not publish.yml, which only uploads the Python package to PyPI on a release. container.yml is delivery, not a gate: it does not gate a pull request, and a red run there means the image is missing, not that the code is broken.

docker run --rm -p 8000:8000 \
  --read-only --tmpfs /tmp \
  --cap-drop=ALL --security-opt=no-new-privileges \
  ghcr.io/malkreide/wsl-envidat-mcp:latest

Kubernetes hardening (excerpt):

securityContext:
  runAsNonRoot: true
  runAsUser: 1000
  readOnlyRootFilesystem: true
  allowPrivilegeEscalation: false
  capabilities: { drop: ["ALL"] }

Available Tools

Example Use Cases

Resources

Valid domain values: wald, biodiversitaet, naturgefahren, schnee_eis, landschaft

Architecture

┌─────────────────┐     ┌───────────────────────────┐     ┌──────────────────────────┐
│   Claude / AI   │────▶│    WSL EnviDat MCP        │────▶│       envidat.ch          │
│   (MCP Host)    │◀────│    (MCP Server)           │◀────│                          │
└─────────────────┘     │                           │     │  CKAN API  (REST/JSON)   │
                        │  10 Tools · 2 Resources   │     │  Solr full-text search   │
                        │  Stdio | Streamable HTTP  │     │  1,000+ research datasets│
                        │                           │     │  815+ open datasets      │
                        │  server.py                │     │  Time series since 1890  │
                        │  api_client.py            │     └──────────────────────────┘
                        └───────────────────────────┘

Infrastructure Components

Project Structure