Starfetch

Starfetch gives AI agents safe, reproducible access to public astronomy catalogs through the Model Context Protocol (MCP).

Ask an astronomy question in natural language. A Starfetch-enabled agent can select an appropriate service, inspect its live tables and columns, construct a bounded ADQL query, execute it, and return the result with the exact service, table, query, limit, units, and assumptions it used.

You: Find the 10 Gaia DR3 sources with the highest proper motion within 0.5
     degrees of the Pleiades center at RA 56.75°, Dec +24.12°. What stands out?

Agent: selects Gaia → inspects metadata → runs bounded ADQL →
       returns catalog rows, exact ADQL, units, and assumptions

Representative captured result:

Service: ESA Gaia Archive
Table: gaiadr3.gaia_source
Rows returned: 10
Query limit: TOP 10 / MAXREC 10

Highest returned proper motions:
- Gaia DR3 66780900298410496: 244.48 mas/yr
- Gaia DR3 66524409149512064: 184.50 mas/yr

Exact ADQL:
SELECT TOP 10 source_id, ra, dec, pm, pmra, pmdec, parallax, parallax_error,
  phot_g_mean_mag, bp_rp, ruwe
FROM gaiadr3.gaia_source
WHERE CONTAINS(POINT('ICRS', ra, dec), CIRCLE('ICRS', 56.75, 24.12, 0.5)) = 1
  AND pm IS NOT NULL
ORDER BY pm DESC

Gaia, SIMBAD, VizieR, the NASA Exoplanet Archive, and IRSA are available as built-in service presets. Agents can also discover and query other public TAP services by URL. Starfetch remains TAP-native and keeps ADQL visible, so the agent workflow is convenient without becoming a scientific black box.

Start here

• Connect an agent
• What the agent does
• Why Starfetch?
• Reliability without hidden assumptions
• Optional Starfetch skill
• Supported scope
• CLI and TypeScript
• Run the Gaia proper-motion demo
• Development

Connect an agent

The local npm server is Starfetch's stable public connection path. It runs on your computer and can be registered with any compatible MCP client.

Local npm server

Register Starfetch with the agent client that will launch it. Running the MCP package by itself only starts a stdio server; it does not connect that server to an agent. Starfetch is also discoverable through the official MCP Registry.

Codex

Register Starfetch for the Codex CLI, IDE extension, and ChatGPT desktop app:

codex mcp add starfetch -- npx -y @starfetch-js/mcp
codex mcp list

These Codex surfaces share MCP configuration. See the official Codex MCP documentation.

Claude Code

Register Starfetch in user scope:

claude mcp add --scope user --transport stdio starfetch -- npx -y @starfetch-js/mcp
claude mcp get starfetch

See the official Claude Code MCP documentation.

Cursor

Add this server entry to ~/.cursor/mcp.json for global use or .cursor/mcp.json for one project:

{
  "mcpServers": {
    "starfetch": {
      "command": "npx",
      "args": ["-y", "@starfetch-js/mcp"]
    }
  }
}

See the official Cursor MCP documentation.

Other MCP clients can use the same stdio command and arguments through their own server-registration interface:

command: npx
args: -y @starfetch-js/mcp

Restart or reload the client after registration, then ask a normal astronomy question. You should not need to write ADQL or name Starfetch tools in the prompt. Starfetch requires Node.js 22 or newer.

Remote MCP

A remote MCP service runs on the internet and accepts MCP connections over HTTPS. No Starfetch package has to be installed or launched on the user's computer. Compatible clients can connect to the production endpoint:

https://starfetch-mcp-app-production-wumkqeiqaa-ew.a.run.app/mcp

The remote service is public, unauthenticated, bounded, and best effort. Do not send credentials, private archive URLs, sensitive personal data, or unbounded workloads. The local npm server remains the default when you want local control or when a client does not support remote Streamable HTTP MCP.

See the remote service's overview, privacy notice, support, and terms of use.

What the agent does

For a service-specific catalog question, Starfetch guidance teaches the agent to:

1. choose an explicit service preset or public TAP URL;
2. check service availability when appropriate;
3. inspect relevant tables and the selected table's columns;
4. construct ADQL only from discovered schema information;
5. bound exploratory work with ADQL TOP, TAP MAXREC, or both;
6. execute the smallest useful query;
7. return the service, table, exact ADQL, effective limit, format, units, and relevant assumptions;
8. return to metadata after a schema or syntax failure instead of guessing.

The agent should never present a timeout, availability failure, parse error, or query error as an empty scientific result. A successful zero-row result and a failed request are different outcomes.

Starfetch exposes tools for the complete workflow:

starfetch_list_presets
starfetch_registry_search
starfetch_tap_availability
starfetch_tap_capabilities
starfetch_tap_tables
starfetch_tap_columns
starfetch_tap_query
starfetch_tap_submit_job
starfetch_tap_job_status
starfetch_tap_job_wait
starfetch_tap_job_fetch
starfetch_tap_job_delete

Query tools return result data separately from diagnostics. They preserve the exact submitted ADQL and effective row limit for reproduction and review. Synchronous queries and async submissions send TAP MAXREC=100 when maxrec is omitted.

The remote HTTP service returns an opaque jobCapability with each async submission. Supply that value unchanged to every remote status, wait, fetch, or delete call. The capability remains usable while the remote job exists and the service signing secret is unchanged. Remote deletion is marked destructive so MCP clients can obtain user approval. The stdio MCP server and CLI keep their existing job-reference behavior and do not advertise remote-only fields.

Why Starfetch?

Starfetch is a useful middle layer when an agent needs live public catalog data without turning the workflow into a black box:

• it inspects live schemas instead of guessing table and column names;
• it bounds public-service queries by default and preserves service failures;
• it returns exact ADQL, limits, units, and assumptions for reproduction;
• it provides one metadata-first interface across several TAP services; and
• its CLI and TypeScript library can reproduce an agent's query outside the agent client.

Use an archive's own interface, PyVO/Astropy, or local analysis tools instead when you need authenticated/private archives, extensive local analysis, image data processing, or authoritative astrophysical interpretation.

Reliability without hidden assumptions

The MCP server works without installing a filesystem skill. Starfetch carries the same canonical guidance through three overlapping layers:

The server exposes the retrievable prompts query_astronomy_catalog, explore_service, run_cone_search, and troubleshoot_adql. Canonical Markdown resources are available under starfetch://guides/, starfetch://services/, and starfetch://examples/.

Prompt and resource support depends on the MCP client. Tool descriptions remain self-sufficient for basic safe operation when a client exposes tools only. The optional skill contains the full workflow, service references, and examples.

Optional Starfetch skill