
AlphaFold
STDIOAlphaFold MCP server for accessing protein structure predictions and analysis tools.
AlphaFold MCP server for accessing protein structure predictions and analysis tools.
A comprehensive Model Context Protocol (MCP) server that provides access to the AlphaFold Protein Structure Database through a rich set of tools and resources for protein structure prediction analysis.
This MCP server enables seamless integration with AlphaFold's vast collection of protein structure predictions, offering tools for structure retrieval, confidence analysis, batch processing, and visualization preparation. Perfect for researchers, bioinformaticians, and structural biologists working with predicted protein structures.
# Clone or create the server directory npm install # Build the server npm run build
Add to your MCP configuration:
{ "mcpServers": { "alphafold-server": { "command": "node", "args": ["/path/to/alphafold-server/build/index.js"] } } }
# Start the server npm start # Or run directly node build/index.js
get_structure
Retrieve AlphaFold structure prediction for a specific UniProt ID.
Parameters:
uniprotId
(required): UniProt accession (e.g., "P21359", "Q8N726")format
(optional): Output format - "pdb", "cif", "bcif", or "json" (default: "json")Example:
{ "uniprotId": "P04637", "format": "json" }
download_structure
Download AlphaFold structure file in specified format.
Parameters:
uniprotId
(required): UniProt accessionformat
(optional): File format - "pdb", "cif", or "bcif" (default: "pdb")check_availability
Check if AlphaFold structure prediction is available for a UniProt ID.
Parameters:
uniprotId
(required): UniProt accession to checksearch_structures
Search for available AlphaFold structures by protein name or gene.
Parameters:
query
(required): Search term (protein name, gene name, etc.)organism
(optional): Filter by organismsize
(optional): Number of results (1-100, default: 25)list_by_organism
List all available structures for a specific organism.
Parameters:
organism
(required): Organism name (e.g., "Homo sapiens", "Escherichia coli")size
(optional): Number of results (1-100, default: 50)get_organism_stats
Get statistics about AlphaFold coverage for an organism.
Parameters:
organism
(required): Organism nameget_confidence_scores
Get per-residue confidence scores for a structure prediction.
Parameters:
uniprotId
(required): UniProt accessionthreshold
(optional): Confidence threshold (0-100)analyze_confidence_regions
Analyze confidence score distribution and identify high/low confidence regions.
Parameters:
uniprotId
(required): UniProt accessionget_prediction_metadata
Get metadata about the prediction including version, date, and quality metrics.
Parameters:
uniprotId
(required): UniProt accessionbatch_structure_info
Get structure information for multiple proteins simultaneously.
Parameters:
uniprotIds
(required): Array of UniProt accessions (max 50)format
(optional): Output format - "json" or "summary" (default: "json")batch_download
Download multiple structure files.
Parameters:
uniprotIds
(required): Array of UniProt accessions (max 20)format
(optional): File format - "pdb" or "cif" (default: "pdb")batch_confidence_analysis
Analyze confidence scores for multiple proteins.
Parameters:
uniprotIds
(required): Array of UniProt accessions (max 30)compare_structures
Compare multiple AlphaFold structures for analysis.
Parameters:
uniprotIds
(required): Array of UniProt accessions to compare (2-10)find_similar_structures
Find AlphaFold structures similar to a given protein.
Parameters:
uniprotId
(required): Reference UniProt accessionorganism
(optional): Filter by organismget_coverage_info
Get information about sequence coverage in the AlphaFold prediction.
Parameters:
uniprotId
(required): UniProt accessionvalidate_structure_quality
Validate and assess the overall quality of an AlphaFold prediction.
Parameters:
uniprotId
(required): UniProt accessionexport_for_pymol
Export structure data formatted for PyMOL visualization.
Parameters:
uniprotId
(required): UniProt accessionincludeConfidence
(optional): Include confidence score coloring (default: true)export_for_chimerax
Export structure data formatted for ChimeraX visualization.
Parameters:
uniprotId
(required): UniProt accessionincludeConfidence
(optional): Include confidence score coloring (default: true)get_api_status
Check AlphaFold API status and database statistics.
Parameters: None
alphafold://structure/{uniprotId}
MIME Type: application/json
Description: Complete AlphaFold structure prediction for a UniProt ID
alphafold://pdb/{uniprotId}
MIME Type: chemical/x-pdb
Description: PDB format structure file for a UniProt ID
alphafold://confidence/{uniprotId}
MIME Type: application/json
Description: Per-residue confidence scores for a structure prediction
alphafold://summary/{organism}
MIME Type: application/json
Description: Summary of all available structures for an organism
// 1. Check if structure is available await use_mcp_tool("alphafold-server", "check_availability", { uniprotId: "P04637", }); // 2. Get structure metadata await use_mcp_tool("alphafold-server", "get_prediction_metadata", { uniprotId: "P04637", }); // 3. Analyze confidence scores await use_mcp_tool("alphafold-server", "get_confidence_scores", { uniprotId: "P04637", threshold: 70, });
// Compare multiple related proteins await use_mcp_tool("alphafold-server", "compare_structures", { uniprotIds: ["P04637", "P53350", "P63151"], }); // Batch confidence analysis await use_mcp_tool("alphafold-server", "batch_confidence_analysis", { uniprotIds: ["P04637", "P53350", "P63151"], });
// Export for PyMOL with confidence coloring await use_mcp_tool("alphafold-server", "export_for_pymol", { uniprotId: "P04637", includeConfidence: true, }); // Export for ChimeraX await use_mcp_tool("alphafold-server", "export_for_chimerax", { uniprotId: "P04637", includeConfidence: true, });
// Get human protein statistics await use_mcp_tool("alphafold-server", "get_organism_stats", { organism: "Homo sapiens", }); // List available structures await use_mcp_tool("alphafold-server", "list_by_organism", { organism: "Homo sapiens", size: 100, });
The server connects to the AlphaFold API at https://alphafold.ebi.ac.uk/api/
and provides structured access to:
The server includes comprehensive error handling for:
Please be mindful of API usage:
Contributions are welcome! Please ensure:
Developed by Augmented Nature - augmentednature.ai
Augmented Nature specializes in creating AI-powered tools and infrastructure for scientific research and data analysis.
MIT License - see LICENSE file for details.
If you use this MCP server in your research, please cite:
For issues and questions:
Note: This server provides a convenient interface to AlphaFold data but does not store or cache structure data. All data is retrieved directly from the official AlphaFold API.