MeteoSwiss MCP Server
Swiss weather data for AI assistants — forecasts, measurements, stations, pollen.
Que pouvez-vous faire avec MeteoSwiss MCP Server ?
- Multi-day forecasts for ~6000 Swiss locations (postal codes, stations, place names)
- Real-time measurements from ~300 stations (~160 full weather + ~140 precipitation-only), updated every 10 minutes
- Station discovery by name, canton, or GPS coordinates
- Pollen monitoring from ~15 stations across Switzerland
- Climate series from the National Basic Climatic Network (NBCN), going back decades
- MeteoSwiss website search and content retrieval
Installation
Ajoutez ceci à la configuration MCP de votre client (Claude Desktop : claude_desktop_config.json ; Cursor : .cursor/mcp.json ; VS Code et les autres utilisent la même forme « mcpServers »).
claude mcp add --transport http meteoswiss-mcp-server 'https://meteoswiss-mcp.ars.is/mcp'{
"mcpServers": {
"meteoswiss-mcp-server": {
"type": "http",
"url": "https://meteoswiss-mcp.ars.is/mcp"
}
}
}meteoswiss-mcpclaude mcp add meteoswiss-mcp-server -- npx -y meteoswiss-mcp@2.0.2{
"mcpServers": {
"meteoswiss-mcp-server": {
"command": "npx",
"args": [
"-y",
"meteoswiss-mcp@2.0.2"
]
}
}
}Documentation
Tirée du README du projet sur GitHub, actualisée chaque semaine. Les liens ouvrent GitHub.
Sommaire
🌦️ MeteoSwiss LLM Tools
Swiss weather data for AI assistants — powered by MeteoSwiss Open Government Data (OGD), the same data behind the MeteoSwiss app and website. Free, no API key required.
meteoswiss-mcp.ars.is — try the hosted service instantly, no setup needed.
This repo is also a working answer to a design question: how should you give AI agents access to a public dataset? It implements the same MeteoSwiss data access twice — as an agent skill (markdown instructions plus bash scripts, no server) and as an MCP server (structured tools, fuzzy matching, caching, hosted). The two approaches are compared honestly in the skill vs. MCP case study.
A third piece, meteoswiss-forecast-evals, demonstrates eval-driven interface design: a promptfoo suite measuring how well 13 LLMs read the forecast JSON, which settled a real design decision — local-time timestamps beat UTC, with hour-level lookups scoring ~100% vs. ~0%.
What the tools provide:
- Multi-day forecasts for ~6000 Swiss locations (postal codes, stations, place names)
- Real-time measurements from ~300 stations (~160 full weather + ~140 precipitation-only), updated every 10 minutes
- Station discovery by name, canton, or GPS coordinates
- Pollen monitoring from ~15 stations across Switzerland
- Climate series from the National Basic Climatic Network (NBCN), going back decades
- MeteoSwiss website search and content retrieval
What this repo demonstrates
- An agent skill — teach an agent to fetch open data directly with
curl/awk/jq: ~630 lines of markdown and bash, zero infrastructure. → packages/meteoswiss-skills - An MCP server — the same data as structured, validated tools with fuzzy station matching, geocoding, TTL-tiered caching, a real test suite, Docker, and a hosted instance. → packages/meteoswiss-mcp
- Eval-driven interface design — treat tool output as an interface for a language model, and measure its legibility before shipping. → packages/meteoswiss-forecast-evals
Read the comparison: Skill vs. MCP Server: Two Ways to Give AI Agents the Same Data.
Choose your approach
Both approaches answer the same weather questions. Which to install depends on your agent:
| MCP Server | Agent Skill | |
|---|---|---|
| What it is | Standalone server exposing 7 structured tools via MCP | Markdown instructions + 5 bash scripts the agent runs directly |
| Works with | Claude Desktop, Claude.ai, Cursor, any MCP client | Claude Code, Cursor, any agent with shell access |
| Coverage | Forecasts, current weather, stations, pollen, climate series, website search | Forecasts, current weather, stations, pollen |
| Extras | Fuzzy matching, geocoding, caching, DE/FR/IT prompts, structured JSON | No server, no Node.js — just curl, awk, jq |
| Size | ~6.6k lines TypeScript, tested in CI | ~630 lines markdown + bash |
| Install | One-liner (hosted), npm, or Docker | Plugin marketplace, Skills CLI, or symlink |
Full comparison — parity matrix, engineering trade-offs, context cost, when to choose which: docs/skill-vs-mcp.md.
MCP server — quickstart
Use the hosted instance (no installation):
# Claude Code
claude mcp add meteoswiss https://meteoswiss-mcp.ars.is/mcpFor Cursor, install from the Cursor Directory or add manually via Settings → MCP.
Or self-host with Docker:
docker run -p 3000:3000 ghcr.io/eins78/meteoswiss-mcp:latestSee the meteoswiss-mcp README for Claude Desktop setup, environment variables, and full documentation.
Agent skill — quickstart
Install via the Claude Code plugin marketplace:
/plugin marketplace add eins78/meteoswiss-llm-tools
/plugin install meteoswiss-skills@meteoswiss-marketplaceOr with the Skills CLI:
pnpx skills add https://github.com/eins78/meteoswiss-llm-tools.git#packages/meteoswiss-skills --global --agent claude-code --allSee the meteoswiss-skills README for manual installation and details.
Available tools (MCP server)
| Tool | Description |
|---|---|
meteoswissLocalForecast | Multi-day forecasts by postal code, station, or place name |
meteoswissCurrentWeather | Real-time measurements (temperature, wind, humidity, pressure) |
meteoswissStations | Search station network by name, canton, or coordinates |
meteoswissPollenData | Pollen concentration data from monitoring stations |
meteoswissClimateData | NBCN climate series — temperature, precipitation, sunshine, and climate indicators going back decades |
search | Search MeteoSwiss website content (DE, FR, IT, EN) |
fetch | Fetch full content from MeteoSwiss pages |
Example questions
Works with both approaches — just ask in any of Switzerland's four languages:
- "What's the weather forecast for Zurich this week?"
- "Wie wird das Wetter in Bern morgen?"
- "Quelle est la météo à Genève?"
- "Che tempo fa a Lugano?"
Packages
| Package | Version | Description |
|---|---|---|
meteoswiss-mcp | MCP server with structured tools, fuzzy matching, and geocoding | |
meteoswiss-skills | 1.0.0 | Agent skill — direct HTTP access, no server needed |
meteoswiss-forecast-evals | — | LLM eval suite for the forecast JSON format (standalone, not a workspace member) |
Documentation
- Skill vs. MCP case study — the honest comparison of the two approaches
- Eval results: forecast JSON comprehension — the local-time-vs-UTC sweep
- MCP server user guide
- Documentation index
Development
git clone https://github.com/eins78/meteoswiss-llm-tools.git
cd meteoswiss-llm-tools
nvm use && pnpm installSee each package's README for package-specific commands. The repo uses changesets for versioning.
Manual, point-in-time test reports (e.g. live MCP tool test passes) live in docs/test-reports/.
Data source
All weather data comes from MeteoSwiss Open Government Data (OGD) — the official free data offering from Switzerland's Federal Office of Meteorology and Climatology. The same data powers the MeteoSwiss app and website.
License
CC0-1.0 — public domain
Questions fréquentes
Qu'est-ce que MeteoSwiss MCP Server ?
Swiss weather data for AI assistants — forecasts, measurements, stations, pollen.
Comment installer MeteoSwiss MCP Server ?
Il fonctionne comme serveur distant. Dans Claude Code : claude mcp add --transport http meteoswiss-mcp-server 'https://meteoswiss-mcp.ars.is/mcp'. Pour les autres clients, collez la configuration JSON de cette page dans leurs réglages MCP.
Faut-il une clé API pour MeteoSwiss MCP Server ?
Son entrée au registre n'indique aucune clé ni secret obligatoire. Certaines fonctions peuvent tout de même demander un compte auprès du service auquel il se connecte.
MeteoSwiss MCP Server est-il open source ?
Oui. Son code est sur GitHub sous licence CC0-1.0.
Qui maintient MeteoSwiss MCP Server ?
Il est publié par eins78. AI.tn le référence sans le maintenir ni le recommander.
Issu du registre MCP officiel. Être listé n'est pas une recommandation : vérifiez le code et les permissions d'un serveur avant de lui donner accès à vos données.