A comprehensive Model Context Protocol (MCP) server providing advanced access to the UniProt protein database. This server offers 26 specialized bioinformatics tools enabling AI assistants and MCP clients to perform sophisticated protein research, comparative genomics, structural biology analysis, and systems biology investigations directly through UniProt's REST API.
Developed by Augmented Nature
- Protein Search: Search the UniProt database by protein name, keywords, or organism
- Detailed Protein Info: Retrieve comprehensive protein information including function, structure, and annotations
- Gene-based Search: Find proteins by gene name or symbol
- Sequence Retrieval: Get amino acid sequences in FASTA or JSON format
- Feature Analysis: Access functional domains, active sites, binding sites, and other protein features
- Protein Comparison: Side-by-side comparison of multiple proteins with sequence and feature analysis
- Homolog Discovery: Find homologous proteins across different species
- Ortholog Identification: Identify orthologous proteins for evolutionary studies
- Phylogenetic Analysis: Retrieve evolutionary relationships and phylogenetic data
- 3D Structure Information: Access PDB references and structural data
- Advanced Domain Analysis: Enhanced domain analysis with InterPro, Pfam, and SMART annotations
- Variant Analysis: Disease-associated variants and mutations
- Sequence Composition: Amino acid composition, hydrophobicity, and other sequence properties
- Pathway Integration: Associated biological pathways from KEGG and Reactome
- Protein Interactions: Protein-protein interaction networks
- Functional Classification: Search by GO terms or functional annotations
- Subcellular Localization: Find proteins by subcellular localization
- Batch Processing: Efficiently process multiple protein accessions
- Advanced Search: Complex queries with multiple filters (length, mass, organism, function)
- Taxonomic Classification: Search by detailed taxonomic classification
- External Database Links: Links to PDB, EMBL, RefSeq, Ensembl, and other databases
- Literature References: Associated publications and citations
- Annotation Quality: Quality scores and confidence levels for different annotations
- Specialized Export: Export data in GFF, GenBank, EMBL, and XML formats
- Accession Validation: Verify UniProt accession number validity
- Taxonomic Information: Detailed taxonomic classification and lineage data
- Direct access to protein data via URI templates for seamless integration
- Node.js (v16 or higher)
- npm or yarn
- Clone the repository:
git clone <repository-url>
cd uniprot-server- Install dependencies:
npm install- Build the project:
npm run buildBuild the Docker image:
docker build -t uniprot-mcp-server .Run the container:
docker run -i uniprot-mcp-serverFor MCP client integration, you can use the container directly:
{
"mcpServers": {
"uniprot": {
"command": "docker",
"args": ["run", "-i", "uniprot-mcp-server"],
"env": {}
}
}
}Create a docker-compose.yml for easier management:
version: "3.8"
services:
uniprot-mcp:
build: .
image: uniprot-mcp-server
stdin_open: true
tty: trueRun with:
docker-compose upThe server is designed to run as an MCP server that communicates via stdio:
npm startAdd the server to your MCP client configuration (e.g., Claude Desktop):
{
"mcpServers": {
"uniprot": {
"command": "node",
"args": ["/path/to/uniprot-server/build/index.js"],
"env": {}
}
}
}Search the UniProt database for proteins by name, keyword, or organism.
Parameters:
query(required): Search query (protein name, keyword, or complex search)organism(optional): Organism name or taxonomy ID to filter resultssize(optional): Number of results to return (1-500, default: 25)format(optional): Output format - json, tsv, fasta, xml (default: json)
Example:
{
"query": "insulin",
"organism": "human",
"size": 5
}Get detailed information for a specific protein by UniProt accession.
Parameters:
accession(required): UniProt accession number (e.g., P04637)format(optional): Output format - json, tsv, fasta, xml (default: json)
Example:
{
"accession": "P01308",
"format": "json"
}Search for proteins by gene name or symbol.
Parameters:
gene(required): Gene name or symbol (e.g., BRCA1, INS)organism(optional): Organism name or taxonomy ID to filter resultssize(optional): Number of results to return (1-500, default: 25)
Example:
{
"gene": "BRCA1",
"organism": "human"
}Get the amino acid sequence for a protein.
Parameters:
accession(required): UniProt accession numberformat(optional): Output format - fasta, json (default: fasta)
Example:
{
"accession": "P01308",
"format": "fasta"
}Get functional features and domains for a protein.
Parameters:
accession(required): UniProt accession number
Example:
{
"accession": "P01308"
}The server provides direct access to UniProt data through URI templates:
- URI:
uniprot://protein/{accession} - Description: Complete protein information for a UniProt accession
- Example:
uniprot://protein/P01308
- URI:
uniprot://sequence/{accession} - Description: FASTA format protein sequence
- Example:
uniprot://sequence/P01308
- URI:
uniprot://search/{query} - Description: Search results for proteins matching the query
- Example:
uniprot://search/insulin
Search for insulin proteins in humans:
// Tool call
{
"tool": "search_proteins",
"arguments": {
"query": "insulin",
"organism": "human",
"size": 10
}
}Retrieve comprehensive information about human insulin:
// Tool call
{
"tool": "get_protein_info",
"arguments": {
"accession": "P01308"
}
}Find proteins associated with the BRCA1 gene:
// Tool call
{
"tool": "search_by_gene",
"arguments": {
"gene": "BRCA1",
"organism": "human"
}
}Get the amino acid sequence for human insulin:
// Tool call
{
"tool": "get_protein_sequence",
"arguments": {
"accession": "P01308",
"format": "fasta"
}
}Get functional domains and features for human insulin:
// Tool call
{
"tool": "get_protein_features",
"arguments": {
"accession": "P01308"
}
}This server integrates with the UniProt REST API for programmatic access to protein data. For more information about UniProt:
- UniProt Website: https://www.uniprot.org/
- API Documentation: https://www.uniprot.org/help/api
- REST API Guide: https://www.uniprot.org/help/api_queries
All API requests include:
- User-Agent:
UniProt-MCP-Server/1.0.0 - Timeout: 30 seconds
- Base URL:
https://rest.uniprot.org(programmatic access only)
The server includes comprehensive error handling:
- Input Validation: All parameters are validated using type guards
- API Errors: Network and API errors are caught and returned with descriptive messages
- Timeout Handling: Requests timeout after 30 seconds
- Graceful Degradation: Partial failures are handled appropriately
npm run buildRun TypeScript compiler in watch mode:
npm run devuniprot-server/
├── src/
│ └── index.ts # Main server implementation
├── build/ # Compiled JavaScript output
├── package.json # Node.js dependencies and scripts
├── tsconfig.json # TypeScript configuration
└── README.md # This file
- @modelcontextprotocol/sdk: Core MCP SDK for server implementation
- axios: HTTP client for UniProt API requests
- typescript: TypeScript compiler for development
MIT License
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests if applicable
- Submit a pull request
For issues and questions:
- Check the UniProt API documentation
- Review the Model Context Protocol specification
- Open an issue on the repository
This comprehensive UniProt MCP Server is developed by Augmented Nature, a leading innovator in AI-powered bioinformatics and computational biology solutions. Augmented Nature specializes in creating advanced tools that bridge the gap between artificial intelligence and biological research, enabling researchers to unlock deeper insights from biological data.
search_proteins- Search UniProt database by name, keyword, or organismget_protein_info- Get detailed protein information by accessionsearch_by_gene- Find proteins by gene name or symbolget_protein_sequence- Retrieve amino acid sequencesget_protein_features- Access functional features and domains
compare_proteins- Compare multiple proteins side-by-sideget_protein_homologs- Find homologous proteins across speciesget_protein_orthologs- Identify orthologous proteinsget_phylogenetic_info- Retrieve evolutionary relationships
get_protein_structure- Access 3D structure information from PDBget_protein_domains_detailed- Enhanced domain analysis (InterPro, Pfam, SMART)get_protein_variants- Disease-associated variants and mutationsanalyze_sequence_composition- Amino acid composition analysis
get_protein_pathways- Associated biological pathways (KEGG, Reactome)get_protein_interactions- Protein-protein interaction networkssearch_by_function- Search by GO terms or functional annotationssearch_by_localization- Find proteins by subcellular localization
batch_protein_lookup- Process multiple accessions efficientlyadvanced_search- Complex queries with multiple filterssearch_by_taxonomy- Search by taxonomic classification
get_external_references- Links to other databases (PDB, EMBL, RefSeq, etc.)get_literature_references- Associated publications and citationsget_annotation_confidence- Quality scores for annotations
export_protein_data- Export in specialized formats (GFF, GenBank, EMBL, XML)validate_accession- Check accession number validityget_taxonomy_info- Detailed taxonomic information
- Major expansion: Added 21 new specialized tools (total: 26 tools)
- Comparative Analysis: Protein comparison, homolog/ortholog identification, phylogenetic analysis
- Structural Biology: 3D structure integration, detailed domain analysis, variant analysis
- Systems Biology: Pathway integration, protein interactions, functional classification
- Advanced Search: Batch processing, complex filtering, taxonomic search
- Literature Integration: External database links, citations, annotation confidence
- Data Export: Multiple specialized formats (GFF, GenBank, EMBL, XML)
- Enhanced Docker Support: Multi-stage builds with security best practices
- Comprehensive Documentation: Complete tool reference and examples
- Developed by Augmented Nature: Professional bioinformatics platform
If you use this project in your research or publications, please cite it as follows:
author = {Moudather Chelbi},
title = {UniProt MCP Server},
year = {2025},
howpublished = {https://github.com/Augmented-Nature/Augmented-Nature-UniProt-MCP-Server/},
note = {Accessed: 2025-06-29}