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.

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

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

2. 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

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

• Issues: GitHub Issues
• Documentation: Full Documentation
• Discussions: GitHub Discussions

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

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

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"
)

License

[Specify your license here]