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DESI MCP Server

@SandyYuan

代码仓库

基本信息

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其他

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stdio

作者

SandyYuan

关于 DESI MCP Server

mcp server for desi and other surveys

#astro-mcp#mcp-server#desi-survey

DESI MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "astro_mcp": {
      "command": "python",
      "args": [
        "server.py"
      ]
    }
  }
}

DESI MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is DESI MCP Server?

An MCP server providing fast, comprehensive access to DESI (Dark Energy Spectroscopic Instrument) survey data through dual access methods: Data Lab SQL queries (default) and SPARCL client (backup). It is designed for AI assistants and researchers to search and retrieve DESI spectroscopic data.

How to use DESI MCP Server?

Install dependencies with pip install mcp sparclclient datalab. Run the server with python server.py. Configure as an MCP client using the JSON snippet provided. Invoke tools via MCP, e.g., search_objects(ra=9.9443, dec=41.7221, object_types=["GALAXY"]) or get_spectrum_by_id(sparcl_id="1270d3c4-9d36-11ee-94ad-525400ad1336", format="full").

Key features of DESI MCP Server

  • Accurate distance sorting for coordinate searches
  • No result limits via Data Lab SQL
  • Fast queries with Q3C spatial indexing
  • Cross-survey access (DESI + BOSS + SDSS) via SPARCL
  • Complete spectral data with wavelength/flux arrays and uncertainties
  • Async support for large datasets (>100k results)

Use cases of DESI MCP Server

  • Search for DESI objects by coordinate (nearest, cone, box)
  • Retrieve full spectrum data by SPARCL UUID for analysis
  • Filter objects by type (galaxy, quasar, star), redshift, and data release
  • Cross‑survey queries including BOSS and SDSS

常见问题

1

What data does DESI MCP Server cover?

DESI DR1 (∼18+ million spectra), DESI EDR (∼1.8 million spectra), and cross‑surveys BOSS DR16, SDSS DR16 via SPARCL. Sky coverage ∼14,000 sq deg, wavelength range 360–980 nm, spectral resolution R∼2000–5500.

2

What are the runtime dependencies?

Requires Python packages: `mcp`, `sparclclient`, `datalab`, plus standard library modules (`asyncio`, `json`, `logging`).

3

Can I query large datasets without limits?

Yes. The default Data Lab SQL method has no result limits; async support is available for datasets larger than 100,000 results.

4

How do I switch between the two access methods?

By default the server uses Data Lab SQL. To use SPARCL for cross‑survey searches, set `use_sparcl_client: true` in your tool call.

5

Does the server sort coordinate search results by distance?

Yes. All coordinate searches are properly sorted by distance for accurate “nearest” results.

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