Open-Meteo MCP Server

A small MCP (Model Context Protocol) server that gives an AI assistant like Claude the ability to look up real weather data — current conditions and forecasts — by wrapping the free Open-Meteo API. No API key required.

Built as a learning project to understand how MCP servers work end to end: defining tools, running them locally, and connecting them to a real AI client.

What it does

Once connected to an MCP-compatible client (like Claude), it exposes three tools:

So a person can ask an assistant "what's the weather in Lisbon tomorrow?" and the assistant will call these tools itself to find out and answer — it isn't pre-programmed with weather data, it fetches it live.

Related MCP server: Weather MCP Server

How it's built

• src/server.ts — defines the three tools and how each one calls the Open-Meteo API. This is the actual "brains" of the server.

• src/index.ts — runs the server over stdio (standard input/output). This is the transport used by local dev tools like the MCP Inspector and by desktop apps that launch the server as a subprocess.

• src/http.ts — runs the same server over Streamable HTTP, so it can be reached over the network — e.g. hosted somewhere, or reached from a browser-based client. This is what makes it possible to demo without installing anything locally.

Both entry points share the same tool definitions in server.ts, so there's only one place where the actual logic lives.

Running it

npm install
npm run build

Local/stdio mode (for tools that spawn the server as a subprocess):

npm start

HTTP mode (for connecting a remote client, e.g. over a forwarded Codespaces port or a hosting provider):

node build/http.js

This starts a plain HTTP server on port 3000 (configurable via PORT) with a single MCP endpoint at POST /mcp, plus a GET /health check.

Testing it without any client

The MCP Inspector's CLI mode is the fastest way to poke at it directly from a terminal, no browser needed:

# See what tools are available
npx @modelcontextprotocol/inspector --cli node build/index.js --method tools/list

# Actually call one
npx @modelcontextprotocol/inspector --cli node build/index.js \
  --method tools/call --tool-name geocode_location --tool-arg name=London

Connecting it to an AI assistant

• Claude Desktop / Claude Code: point at build/index.js as a stdio MCP server in the client's config.

• Claude.ai custom connectors: point at the /mcp URL of a running HTTP instance (requires a plan that allows custom connectors).

• Anthropic API directly: pass the /mcp URL in the mcp_servers parameter of a /v1/messages request — this lets any custom app or page use the tools without needing a pre-built MCP client.

Notes for extending it

Open-Meteo has many more variables and endpoints than this project uses (air quality, marine forecasts, historical/archive data, ensembles). To add more, follow the pattern of an existing tool in src/server.ts — register a new server.tool(...) block with its own input schema and Open-Meteo request.

Data © Open-Meteo.com, CC BY 4.0.