DESI MCP Server

Astro MCP - Agentic Astronomical Data Access

A modular Model Context Protocol (MCP) server that provides unified access to multiple astronomical datasets through a clean, extensible architecture.

This MCP server aims to transform big-data astronomy from a software engineering problem into a natural language conversation. Instead of spending months learning astroquery APIs, researchers simply ask for what they need and get clean, processed analysis-ready data products.

One expert solves the complexity once; thousands of scientists benefit forever. A student with little programming experience can now perform the same multi-survey analysis as an expert astronomer using nothing but natural language and an AI assistant.

This isn't just about astronomy—it's a template for democratizing all of science. Every field has brilliant researchers spending 80% of their time on data wrangling instead of discovery. By removing that bottleneck, we accelerate the pace of scientific progress itself.

The result: AI scientists that can seamlessly access and cross-match data from dozens of astronomical surveys, enabling discoveries that would have taken months of setup to attempt just a few years ago.

Quick Setup for Cursor & Claude Desktop

# Clone the repository git clone https://github.com/SandyYuan/astro_mcp.git cd astro_mcp # Create a dedicated conda environment with Python 3.11+ conda create -n mcp python=3.11 conda activate mcp # Install dependencies pip install -r requirements.txt # Install astronomical libraries for full functionality pip install sparclclient datalab astropy astroquery

# Test basic functionality python test_server.py # Test with a simple query (optional) python -c " import asyncio from server import astro_server async def test(): result = astro_server.get_global_statistics() print('✅ Server working:', result['total_files'], 'files in registry') services = astro_server.list_astroquery_services() print(f'✅ Astroquery: {len(services)} services discovered') asyncio.run(test()) "

Add this configuration to your Cursor MCP settings:

{ "mcpServers": { "astro-mcp": { "command": "/path/to/conda/envs/mcp/bin/python", "args": ["/path/to/astro_mcp/server.py"], "cwd": "/path/to/astro_mcp", "env": {} } } }

conda activate mcp which python # Copy this path for the "command" field above

Edit your Claude Desktop MCP configuration file:

macOS:~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:%APPDATA%\Claude\claude_desktop_config.json

{ "mcpServers": { "astro-mcp": { "command": "/path/to/conda/envs/mcp/bin/python", "args": ["/path/to/astro_mcp/server.py"], "cwd": "/path/to/astro_mcp", "env": {} } } }

- Restart Cursor/Claude Desktopto load the new MCP server
- Test with a querylike:

- "Search for galaxies near RA=10.68, Dec=41.27"
- "Get Betelgeuse's coordinates from SIMBAD"
- "Find 10 BOSS galaxies around z=0.5 and save as FITS"
- "List available astroquery services"

# Check Python environment conda activate mcp python --version # Should be 3.11+ # Test server manually python server.py # Should start without errors

- Verify the Python path in your config points to the conda environment
- Ensure the working directory (cwd) points to the astro_mcp folder
- Check that all dependencies are installed in the correct environment

# Install optional dependencies for full functionality conda activate mcp pip install sparclclient datalab astropy astroquery h5py

Usage Examples with Cursor/Claude Desktop

Once configured, you can ask natural language questions about astronomical data:

- "Find galaxies near RA=150.5, Dec=2.2 within 0.1 degrees"
- "Search for quasars with redshift between 2 and 3"
- "Get Betelgeuse's exact coordinates from SIMBAD"
- "Find 10 BOSS galaxies around redshift 0.5"

- "Query VizieR for stellar catalogs in the Orion region"
- "Search SDSS for galaxies and save as FITS format"
- "Get object information from multiple astronomical databases"
- "List all available astroquery services for galaxy studies"

- "Get the spectrum for DESI object with ID 1270d3c4-9d36-11ee-94ad-525400ad1336"
- "Show me detailed spectral information for the brightest quasar you can find"
- "Find a galaxy spectrum and analyze its redshift"

- "List all saved astronomical data files"
- "Convert my galaxy catalog to FITS format"
- "Preview the structure of the latest search results"
- "Show me storage statistics for downloaded data"

- "Find high-redshift galaxies (z > 1.5) and save their spectra"
- "Search for objects in the COSMOS field and analyze their types"
- "Cross-match DESI and SDSS data for the same sky region"

- Execute appropriate database queries across multiple surveys
- Save results with descriptive filenames and metadata
- Handle coordinate conversions and astronomical calculations
- Convert data to standard formats (CSV, FITS) as needed

astro_mcp/ ├── server.py # Main MCP server entry point ├── data_sources/ # Modular data source implementations │ ├── __init__.py │ ├── base.py # Base class for all data sources │ ├── desi.py # DESI survey data access │ ├── astroquery_universal.py # Universal astroquery wrapper │ └── astroquery_metadata.py # Service metadata and capabilities ├── data_io/ # File handling and conversion │ ├── __init__.py │ ├── preview.py # Data preview and structure analysis │ └── fits_converter.py # FITS format conversion ├── tests/ # Test suite ├── examples/ # Usage examples └── requirements.txt # Project dependencies

- DESI: Dark Energy Spectroscopic Instrument via SPARCL and Data Lab
- Astroquery: Automatic access to 40+ astronomical services (SIMBAD, VizieR, SDSS, Gaia, etc.)
- Auto-discovery: Automatically detects and configures available astroquery services
- Unified interface: Same API for all data sources

- Automatic data saving with descriptive filenames
- Cross-source file registry and organization
- Comprehensive metadata tracking with provenance
- Smart file preview with loading examples
- FITS format conversion for astronomical compatibility

- Coordinate-based searches (point, cone, box) across all surveys
- Object type and redshift filtering
- SQL queries with spatial indexing (Q3C)
- Natural language query interpretation
- Cross-survey data correlation

- Spectral data retrieval and analysis
- Automatic FITS conversion for catalogs, spectra, and images
- File structure inspection and preview
- Statistics and storage management
- Extensible tool architecture for custom analysis

- Parameter preprocessing and validation
- Intelligent error handling with helpful suggestions
- Automatic format detection and conversion
- Consistent metadata across all data sources

Quick Start:For Cursor & Claude Desktop integration, see theQuick Setupsection above.

# Clone the repository git clone https://github.com/SandyYuan/astro_mcp.git cd astro_mcp # Create and activate environment conda create -n mcp python=3.11 conda activate mcp # Install core dependencies pip install -r requirements.txt # Install astronomical libraries pip install sparclclient datalab astropy astroquery # Optional: Install development dependencies pip install pytest coverage

# Test the server components python test_server.py # Check available astroquery services python -c " import asyncio from server import astro_server async def show_services(): services = astro_server.list_astroquery_services() print(f'✅ Discovered {len(services)} astroquery services') for service in services[:5]: # Show first 5 print(f' - {service["full_name"]} ({service["service"]})') asyncio.run(show_services()) "

- search_objects- Find astronomical objects (DESI)
- astroquery_query- Universal queries across 40+ astronomical services
- get_spectrum_by_id- Retrieve detailed spectral data (DESI)

- list_astroquery_services- Show all available astronomical databases
- get_astroquery_service_details- Detailed service information
- search_astroquery_services- Find services by criteria

- preview_data- Inspect saved files with structure analysis
- list_files- Manage saved data across all sources
- file_statistics- Storage usage and organization info
- convert_to_fits- Convert data to FITS format

# Get object coordinates from SIMBAD astroquery_query( service_name="simbad", object_name="Betelgeuse" ) # Search SDSS for galaxies with SQL astroquery_query( service_name="sdss", query_type="query_sql", sql="SELECT TOP 10 ra, dec, z FROM SpecObj WHERE class='GALAXY' AND z BETWEEN 0.1 AND 0.3" ) # Search VizieR catalogs astroquery_query( service_name="vizier", ra=10.68, dec=41.27, radius=0.1 ) # Convert results to FITS convert_to_fits( identifier="search_results.csv", data_type="catalog" )

DESI (Dark Energy Spectroscopic Instrument)

- SPARCL Access: Full spectral data retrieval
- Data Lab SQL: Fast catalog queries (sparcl.main table)
- Coverage: DESI EDR (~1.8M) and DR1 (~18M+ spectra)
- Wavelength: 360-980 nm, Resolution: R ~ 2000-5500

- SIMBAD: Object identification and basic data
- VizieR: Astronomical catalogs and surveys
- SDSS: Sloan Digital Sky Survey data and spectra
- Gaia: Astrometric and photometric data
- MAST: Hubble, JWST, and other space telescope archives
- IRSA: Infrared and submillimeter archives
- ESASky: Multi-mission astronomical data
- And 30+ more services...

- Automatic service discovery and configuration
- Intelligent query type detection
- Parameter preprocessing and validation
- Unified error handling and help generation

# data_sources/my_survey.py from .base import BaseDataSource class MySurveyDataSource(BaseDataSource): def __init__(self, base_dir=None): super().__init__(base_dir=base_dir, source_name="my_survey") # Initialize survey-specific clients def search_objects(self, kwargs): # Implement survey-specific search pass

# server.py from data_sources import MySurveyDataSource class AstroMCPServer: def __init__(self, base_dir=None): # ... existing code ... self.my_survey = MySurveyDataSource(base_dir=base_dir)

# data_sources/astroquery_metadata.py ASTROQUERY_SERVICE_INFO = { "my_service": { "full_name": "My Custom Service", "description": "Custom astronomical database", "data_types": ["catalogs", "images"], "wavelength_coverage": "optical", "object_types": ["stars", "galaxies"], "requires_auth": False, "example_queries": [ { "description": "Search by object name", "query": "astroquery_query(service_name='my_service', object_name='M31')" } ] } }

~/astro_mcp_data/ ├── file_registry.json # Global file registry with metadata ├── desi/ # DESI-specific files │ ├── desi_search_.json # Search results │ ├── spectrum_.json # Spectral data │ └── .fits # FITS conversions └── astroquery/ # Astroquery results ├── astroquery_simbad_.csv # SIMBAD queries ├── astroquery_sdss_.csv # SDSS results ├── astroquery_vizier_.csv # VizieR catalogs └── .fits # FITS conversions

# Run all tests pytest # Test specific modules pytest tests/test_desi.py pytest tests/test_astroquery.py # Test with coverage pytest --cov=data_sources tests/

@software{astro_mcp, title={Astro MCP: Universal Astronomical Data Access for AI Agents}, author={[Your Name]}, year={2024}, url={[Repository URL]} }

- Issues:GitHub Issues
- Documentation:Full Documentation
- Discussions*:GitHub Discussions

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