STRING-MCP

A comprehensive Python package for interacting with the STRING database API through a Model Context Protocol (MCP) bridge.

Install the package in development mode:

"mcpServers": { "string-mcp": { "command": "/path/to/python/env/bin/string-mcp-server", "env": {} } } }

The package provides an MCP server for integration with MCP-compatible clients:

The MCP server provides the following tools:

- map_identifiers: Map protein identifiers to STRING IDs
- get_network_interactions: Get network interactions data
- get_functional_enrichment: Perform functional enrichment analysis
- get_network_image: Generate network visualization images
- get_version_info: Get STRING database version information

The package also provides astring-mcpcommand for standalone usage:

# Run demo string-mcp demo # Get help string-mcp --help # Map protein identifiers string-mcp map TP53 BRCA1 EGFR --species 9606 # Get network interactions string-mcp network TP53 BRCA1 --species 9606 # Generate network image string-mcp image TP53 BRCA1 --output network.png --species 9606

from stringmcp.main import StringDBBridge # Initialize the bridge bridge = StringDBBridge() # Map protein identifiers proteins = ["TP53", "BRCA1", "EGFR"] mapped = bridge.map_identifiers(proteins, species=9606) # 9606 = human # Get network interactions interactions = bridge.get_network_interactions(proteins, species=9606) # Perform functional enrichment enrichment = bridge.get_functional_enrichment(proteins, species=9606)

- Protein Identifier Mapping: Convert various protein identifiers to STRING IDs
- Network Analysis: Retrieve protein-protein interaction networks
- Functional Enrichment: Perform gene ontology and pathway enrichment analysis
- Network Visualization: Generate network images in various formats
- Interaction Partners: Find all interaction partners for proteins
- Functional Annotations: Get detailed functional annotations
- Protein Similarity: Calculate similarity scores between proteins
- PPI Enrichment: Test for protein-protein interaction enrichment
- MCP Integration: Full Model Context Protocol server implementation

- map_identifiers(): Map protein identifiers to STRING IDs
- get_network_interactions(): Get network interaction data
- get_network_image(): Generate network visualization images
- get_interaction_partners(): Find all interaction partners
- get_functional_enrichment(): Perform enrichment analysis
- get_functional_annotation(): Get functional annotations
- get_protein_similarity(): Calculate similarity scores
- get_ppi_enrichment(): Test for PPI enrichment
- get_version_info(): Get STRING database version

The package uses aStringConfigclass for configuration:

from stringmcp.main import StringConfig, StringDBBridge config = StringConfig( base_url="https://string-db.org/api", version_url="https://version-12-0.string-db.org/api", caller_identity="my_app", request_delay=1.0 # Delay between requests in seconds ) bridge = StringDBBridge(config)

The package supports multiple output formats:

- JSON: Structured data (default)
- TSV: Tab-separated values
- XML: XML format
- IMAGE: Network visualization images
- SVG: Scalable vector graphics
- PSI_MI: PSI-MI format

The package supports all species available in STRING. Common species IDs:

- Human: 9606
- Mouse: 10090
- Rat: 10116
- Yeast: 4932
- E. coli: 511145

To use the MCP server with an MCP client, configure it as follows:

{ "mcpServers": { "string-mcp": { "command": "string-mcp-server", "env": {} } } }

- JSON-RPC communication
- Tool discovery and invocation
- Error handling and reporting
- Base64 encoding for image data

# Install in development mode with dev dependencies pip install -e .[dev] # Format code black stringmcp/ # Type checking mypy stringmcp/ # Lint code flake8 stringmcp/

Note: Test files are not currently included in this repository. To add tests, create atests/directory and add test files following the pytest configuration inpyproject.toml.

STRINGmcp/ ├── pyproject.toml # Package configuration and dependencies ├── README.md # This file ├── LICENSE # MIT License ├── .gitignore # Git ignore patterns ├── stringmcp/ # Main package │ ├── __init__.py # Package initialization │ └── main.py # Core STRING API bridge and MCP server └── string_mcp.egg-info/ # Package metadata (generated during install) ├── PKG-INFO # Package information ├── SOURCES.txt # Source files list ├── dependency_links.txt ├── entry_points.txt # CLI entry points ├── requires.txt # Dependencies └── top_level.txt # Top-level package names

MIT License - see LICENSE file for details.
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests
- Run the test suite
- Submit a pull request

For issues and questions, please use the GitHub issue tracker.

This example demonstrates the comprehensive functionality of the STRING-DB MCP bridge by analyzing a set of well-known human DNA repair proteins: TP53, BRCA1, BRCA2, ATM, and ATR.

[ { "queryIndex": 0, "queryItem": "TP53", "stringId": "9606.ENSP00000269305", "ncbiTaxonId": 9606, "taxonName": "Homo sapiens", "preferredName": "TP53", "annotation": "Cellular tumor antigen p53; Acts as a tumor suppressor in many tumor types; induces growth arrest or apoptosis depending on the physiological circumstances and cell type..." }, { "queryIndex": 1, "queryItem": "BRCA1", "stringId": "9606.ENSP00000418960", "ncbiTaxonId": 9606, "taxonName": "Homo sapiens", "preferredName": "BRCA1", "annotation": "Breast cancer type 1 susceptibility protein; E3 ubiquitin-protein ligase that specifically mediates the formation of 'Lys-6'-linked polyubiquitin chains..." }, { "queryIndex": 2, "queryItem": "BRCA2", "stringId": "9606.ENSP00000369497", "ncbiTaxonId": 9606, "taxonName": "Homo sapiens", "preferredName": "BRCA2", "annotation": "Breast cancer type 2 susceptibility protein; Involved in double-strand break repair and/or homologous recombination..." }, { "queryIndex": 3, "queryItem": "ATM", "stringId": "9606.ENSP00000278616", "ncbiTaxonId": 9606, "taxonName": "Homo sapiens", "preferredName": "ATM", "annotation": "Serine-protein kinase ATM; Serine/threonine protein kinase which activates checkpoint signaling upon double strand breaks..." }, { "queryIndex": 4, "queryItem": "ATR", "stringId": "9606.ENSP00000343741", "ncbiTaxonId": 9606, "taxonName": "Homo sapiens", "preferredName": "ATR", "annotation": "Serine/threonine-protein kinase ATR; Serine/threonine protein kinase which activates checkpoint signaling upon genotoxic stresses..." } ]

Examine network interactions between these proteins:

[ { "stringId_A": "9606.ENSP00000269305", "stringId_B": "9606.ENSP00000369497", "preferredName_A": "TP53", "preferredName_B": "BRCA2", "score": 0.995 }, { "stringId_A": "9606.ENSP00000269305", "stringId_B": "9606.ENSP00000343741", "preferredName_A": "TP53", "preferredName_B": "ATR", "score": 0.996 }, { "stringId_A": "9606.ENSP00000269305", "stringId_B": "9606.ENSP00000278616", "preferredName_A": "TP53", "preferredName_B": "ATM", "score": 0.999 }, { "stringId_A": "9606.ENSP00000269305", "stringId_B": "9606.ENSP00000418960", "preferredName_A": "TP53", "preferredName_B": "BRCA1", "score": 0.999 }, { "stringId_A": "9606.ENSP00000278616", "stringId_B": "9606.ENSP00000369497", "preferredName_A": "ATM", "preferredName_B": "BRCA2", "score": 0.995 }, { "stringId_A": "9606.ENSP00000278616", "stringId_B": "9606.ENSP00000418960", "preferredName_A": "ATM", "preferredName_B": "BRCA1", "score": 0.999 }, { "stringId_A": "9606.ENSP00000278616", "stringId_B": "9606.ENSP00000343741", "preferredName_A": "ATM", "preferredName_B": "ATR", "score": 0.999 }, { "stringId_A": "9606.ENSP00000343741", "stringId_B": "9606.ENSP00000369497", "preferredName_A": "ATR", "preferredName_B": "BRCA2", "score": 0.831 }, { "stringId_A": "9606.ENSP00000343741", "stringId_B": "9606.ENSP00000418960", "preferredName_A": "ATR", "preferredName_B": "BRCA1", "score": 0.996 }, { "stringId_A": "9606.ENSP00000369497", "stringId_B": "9606.ENSP00000418960", "preferredName_A": "BRCA2", "preferredName_B": "BRCA1", "score": 0.999 } ]

Key Findings: All interactions show very high confidence scores (>0.8), with most exceeding 0.99, indicating these proteins form a tightly interconnected functional module.

Check if this network is significantly enriched for interactions:

{ "number_of_nodes": 5, "number_of_edges": 10, "average_node_degree": 4.0, "local_clustering_coefficient": 1.0, "expected_number_of_edges": 5, "p_value": 0.0122 }

Statistical Significance: The network shows perfect clustering (coefficient = 1.0) and is significantly enriched for interactions (p = 0.0122), with twice as many edges as expected by chance.

Analyze which biological pathways are enriched in this protein set:

Top DNA Repair Pathways (Selected Results):

[ { "category": "Process", "term": "GO:0071479", "number_of_genes": 5, "preferredNames": ["TP53", "ATM", "ATR", "BRCA2", "BRCA1"], "p_value": 9.72e-13, "fdr": 1.52e-08, "description": "Cellular response to ionizing radiation" }, { "category": "Process", "term": "GO:0042770", "number_of_genes": 5, "preferredNames": ["TP53", "ATM", "ATR", "BRCA2", "BRCA1"], "p_value": 1.69e-11, "fdr": 1.32e-07, "description": "Signal transduction in response to DNA damage" }, { "category": "Process", "term": "GO:0006281", "number_of_genes": 5, "preferredNames": ["TP53", "ATM", "ATR", "BRCA2", "BRCA1"], "p_value": 1.05e-08, "fdr": 1.10e-05, "description": "DNA repair" }, { "category": "KEGG", "term": "hsa03440", "number_of_genes": 3, "preferredNames": ["ATM", "BRCA2", "BRCA1"], "p_value": 8.34e-08, "fdr": 2.80e-05, "description": "Homologous recombination" }, { "category": "KEGG", "term": "hsa04115", "number_of_genes": 3, "preferredNames": ["TP53", "ATM", "ATR"], "p_value": 5.27e-07, "fdr": 5.44e-05, "description": "p53 signaling pathway" } ]

[ { "category": "DISEASES", "term": "DOID:1612", "number_of_genes": 4, "preferredNames": ["TP53", "ATM", "BRCA2", "BRCA1"], "p_value": 5.72e-10, "fdr": 2.02e-06, "description": "Breast cancer" }, { "category": "DISEASES", "term": "DOID:3012", "number_of_genes": 3, "preferredNames": ["TP53", "BRCA2", "BRCA1"], "p_value": 6.59e-10, "fdr": 2.02e-06, "description": "Li-Fraumeni syndrome" } ]

The package can generate protein interaction network visualizations showing evidence-based functional associations.

Example Network Visualization:View Protein Interaction NetworkThis visualization shows the protein-protein interaction network for TP53, BRCA1, BRCA2, ATM, and ATR with high-confidence interactions (score ≥ 400).

Example Enrichment Visualization:View Functional Enrichment Plot

Interface with Biomart, a biological data query tool, using the pybiomart Python package.