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

Restart Cursor/Claude Desktop to load the new MCP server
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

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

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

Fork the repository
Create a feature branch (git checkout -b feature/new-capability)
Add your data source or tool following the existing patterns
Write tests for new functionality
Update documentation and examples
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

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

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

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

Restart Cursor/Claude Desktop to load the new MCP server
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

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

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

Fork the repository
Create a feature branch (git checkout -b feature/new-capability)
Add your data source or tool following the existing patterns
Write tests for new functionality
Update documentation and examples
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

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