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Astro MCP

by SandyYuan
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Python
Hybrid

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.

Vision

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

1. Clone and Setup Environment

# 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

2. Test the Server

# 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()) "

3. Configure for Cursor

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": {} } } }

To find your conda Python path:

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

4. Configure for Claude Desktop

Edit your Claude Desktop MCP configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %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": {} } } }

5. Restart and Test

  1. Restart Cursor/Claude Desktop to load the new MCP server

  2. Test with a query like:

    • "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"

6. Troubleshooting

Server won't start:

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

MCP connection issues:

  • 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

Missing astronomical data:

# 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:

Basic Searches

  • "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"

Multi-Survey Access

  • "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"

Spectral Data Analysis

  • "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"

File Management & Conversion

  • "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"

Advanced Queries

  • "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"

The server will automatically:

  • 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

Architecture

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

Features

🔭 Universal Astronomical Data Access

  • 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

📁 Intelligent File Management

  • 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

🔍 Powerful Search Capabilities

  • 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

📊 Data Analysis & Conversion Tools

  • 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

🤖 AI-Optimized Interface

  • Parameter preprocessing and validation

  • Intelligent error handling with helpful suggestions

  • Automatic format detection and conversion

  • Consistent metadata across all data sources

Installation

Quick Start: For Cursor & Claude Desktop integration, see the Quick Setup section above.

Manual Installation

# 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

Verify Installation

# 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()) "

Quick Start

1. Start the MCP Server

python server.py

2. Available Tools

The server provides these main tools:

Data Access:

  • search_objects - Find astronomical objects (DESI)

  • astroquery_query - Universal queries across 40+ astronomical services

  • get_spectrum_by_id - Retrieve detailed spectral data (DESI)

Service Discovery:

  • list_astroquery_services - Show all available astronomical databases

  • get_astroquery_service_details - Detailed service information

  • search_astroquery_services - Find services by criteria

File Management:

  • 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

3. Example Usage

# 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" )

Data Sources

DESI (Dark Energy Spectroscopic Instrument)

Status: ✅ Fully Implemented

  • 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

Astroquery Universal Access

Status: ✅ Fully Implemented

Major Services Available:

  • 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...

Capabilities:

  • Automatic service discovery and configuration

  • Intelligent query type detection

  • Parameter preprocessing and validation

  • Unified error handling and help generation

Required Dependencies:

pip install astroquery astropy

Extending the Architecture

Adding a New Data Source

  1. Create the data source class:

# 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

  1. Update the main server:

# 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)

Adding New Astroquery Services

The astroquery integration automatically discovers new services. To add custom metadata:

# 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')" } ] } }

File Organization

Files are automatically organized by data source with comprehensive metadata:

~/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

Development

Project Structure Benefits

  • Modularity: Easy to add new surveys and analysis tools

  • Universal Access: Single interface to 40+ astronomical databases

  • Separation of Concerns: Data access, I/O, and analysis are separate

  • Testability: Each module can be tested independently

  • Scalability: Clean architecture supports unlimited growth

Testing

# 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/

Contributing

  1. Fork the repository

  2. Create a feature branch (git checkout -b feature/new-capability)

  3. Add your data source or tool following the existing patterns

  4. Write tests for new functionality

  5. Update documentation and examples

  6. Submit a pull request

Dependencies

Core Requirements

  • mcp>=1.0.0 - Model Context Protocol framework

  • pandas>=2.0.0 - Data manipulation

  • numpy>=1.24.0 - Numerical computing

Astronomical Libraries

  • astroquery>=0.4.6 - Universal astronomical database access

  • astropy>=5.0.0 - FITS files and astronomical calculations

  • sparclclient>=1.0.0 - DESI SPARCL access

  • datalab>=2.20.0 - NOAO Data Lab queries

Optional Features

  • h5py>=3.8.0 - HDF5 file support

  • pytest>=7.0.0 - Testing framework

License

[Specify your license here]

Citation

If you use this software in your research, please cite:

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

Support

Roadmap

Current (v0.1.0)

  • ✅ DESI data access via SPARCL and Data Lab

  • ✅ Universal astroquery integration (40+ services)

  • ✅ Automatic FITS conversion for all data types

  • ✅ Intelligent file management with comprehensive metadata

  • ✅ Natural language query interface

Planned (v0.2.0)

  • 🚧 Cross-survey object matching and correlation

  • 🚧 Advanced astronomical calculations (distances, magnitudes)

  • 🚧 Time-series analysis for variable objects

  • 🚧 Visualization tools integration

Future (v0.3.0+)

  • 🔮 Machine learning integration for object classification

  • 🔮 Real-time data streaming from surveys

  • 🔮 Custom analysis pipeline creation

  • 🔮 Multi-wavelength data correlation tools

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