N2YO Satellite Tracker MCP Server

The N2YO Satellite Tracker MCP server turns the rich, real‑time satellite data from N2YO.com into a conversational, programmatic interface that AI assistants can query on demand. By exposing satellite positions, Two‑Line Element (TLE) data, and pass predictions through the Model Context Protocol, it lets developers embed live space‑tracking capabilities into chatbots, voice assistants, and other AI workflows without the need to build custom integrations.

For developers, this server solves a common pain point: accessing up‑to‑date orbital information in a format that can be understood by language models. Instead of hard‑coding static catalogs or maintaining local TLE repositories, the server pulls fresh data from N2YO’s API on each request. This eliminates lag between launch and availability, supports collision‑avoidance monitoring for amateur radio operators, and enables real‑time visualization of satellite trajectories—all via simple MCP tools.

Key capabilities are grouped around natural language understanding and structured data retrieval. Users can ask questions such as “Which satellites will be over France at 6:00 tonight?” and receive a concise answer. The server also offers fine‑grained filtering by category (military, weather, GPS, amateur, Starlink, space stations), country, or even specific launch dates. For developers needing raw data, tools return full TLE sets and current positions, while pass‑prediction utilities compute visibility windows for any observer location. The radio pass optimization feature is particularly valuable for amateur radio enthusiasts who need precise communication windows.

Typical use cases span hobbyist satellite watching, educational tools that teach orbital mechanics, and operational systems for ground stations. An AI assistant could answer a student’s question about the International Space Station, then hand off the TLE to an external plotting library for a visual orbit. A logistics company could query which satellites will be overhead during a delivery window to avoid interference with radio equipment. In each scenario, the MCP server acts as a lightweight bridge that keeps the assistant’s knowledge base current and accurate.

What sets this MCP server apart is its blend of natural‑language query handling with robust, structured data output. The dual approach lets developers choose the level of detail they need—either a quick answer or full TLEs for downstream processing. Coupled with the ability to configure the API key at runtime via a dedicated tool, it offers both security and flexibility for production deployments. In short, the N2YO Satellite Tracker MCP server equips AI assistants with real‑time space awareness, turning abstract orbital data into actionable insights for developers and end users alike.