Astro MCP

Astro MCP is a purpose‑built Model Context Protocol server that turns the complexity of astronomical data access into a natural‑language conversation. Instead of spending weeks mastering dozens of Python APIs (astroquery, SPARCL, H5py, etc.), researchers can simply ask an AI assistant to retrieve, cross‑match, and preprocess data from any of the major surveys—Gaia, SDSS, DESI, LSST, and many more. The server exposes a clean, extensible set of tools that translate these high‑level requests into efficient backend queries, returning analysis‑ready products such as tables, FITS files, or plotted images.

The core value of Astro MCP lies in democratizing access to big‑data astronomy. By abstracting the underlying data pipelines, a student with no programming background can perform the same multi‑survey analysis that an expert astronomer would spend months configuring. This lowers the barrier to entry for new researchers and accelerates discovery by freeing scientists from tedious data wrangling. The architecture is modular: each survey or service is represented as a resource, and new data sources can be added with minimal effort, making the server future‑proof as new missions launch.

Key capabilities include:

• Unified query interface: A single command set that handles spatial searches, spectral queries, and time‑domain requests across all integrated surveys.
• Cross‑matching engine: Automatic cross‑correlation of objects between catalogs, returning clean tables with merged metadata.
• Data product generation: On‑the‑fly creation of FITS files, CSVs, or visualizations that can be handed back to the AI for further analysis.
• Extensibility hooks: Plug‑in points for custom data sources or preprocessing steps, allowing developers to tailor the server to niche projects.

Typical use cases span a wide spectrum: an astronomer can ask for “10 BOSS galaxies around z = 0.5” and receive a FITS file ready for statistical analysis; an educator can request “plot the color–magnitude diagram of nearby stars” and get a ready‑made chart; a citizen scientist can query “search for supernova candidates in the last 30 days” and get a curated list. In each scenario, the AI assistant becomes a conversational interface to complex data pipelines, turning natural language into reproducible science workflows.

Integration with AI tools is straightforward: the server registers itself as an MCP service, exposing resources such as , , and custom prompt templates. An AI assistant can invoke these resources directly, chain them together in a single conversation, and even store intermediate results for later steps. This tight coupling means developers can build end‑to‑end scientific workflows—query, process, visualize, and publish—without leaving the chat interface. The result is a powerful, developer‑friendly platform that transforms how astronomers interact with data and accelerates the pace of discovery.