SondeHub MCP Server

Overview

The Mcp Sondehub server bridges the gap between AI assistants and real‑time weather balloon data by exposing the SondeHub API through the Model Context Protocol. SondeHub is a global platform that aggregates telemetry from weather balloons, providing rich datasets on atmospheric pressure, temperature, humidity, wind speed, and more. By turning this data into an MCP server, developers can seamlessly query current balloon conditions or historical flight records directly from their AI tools without writing custom HTTP clients.

For developers working with Claude, Gemini, or other MCP‑compatible assistants, this server offers a ready‑made resource that abstracts away authentication, endpoint management, and response parsing. Instead of handling OAuth tokens or constructing complex API calls, the assistant can simply request a “current flight” or “flight by ID” resource and receive structured JSON in return. This reduces boilerplate code, accelerates prototyping, and ensures consistent data handling across projects.

Key capabilities include:

• Resource discovery: The server exposes endpoints for listing available flights, retrieving specific flight data, and accessing metadata such as launch time and location.
• Tool integration: AI assistants can invoke tools that perform filtering (e.g., “show flights above 20 km”) or summarization (“summarize the last 24‑hour flight trend”), leveraging built‑in MCP tool definitions.
• Prompt augmentation: Custom prompts can be attached to resources, enabling assistants to generate context‑aware explanations of balloon trajectories or atmospheric insights.
• Sampling and pagination: Large datasets are handled efficiently, allowing assistants to request paged results or sample subsets without overloading the client.

Real‑world use cases span from meteorological research—where scientists need quick access to the latest atmospheric profiles—to educational platforms that demonstrate real‑time weather phenomena. A flight planning tool could query upcoming balloon launches, while a data science pipeline might ingest SondeHub records for machine learning models predicting weather patterns. In each scenario, the MCP abstraction simplifies integration, letting developers focus on business logic rather than API plumbing.

What sets Mcp Sondehub apart is its lightweight, experiment‑friendly design. It was built primarily to test MCP concepts, yet it already delivers production‑ready features such as authentication handling and structured resource definitions. This makes it an ideal starting point for teams looking to embed live atmospheric data into conversational AI workflows, enabling richer, context‑aware interactions without the overhead of custom API integration.