STRING-MCP
A comprehensive Python package for interacting with the STRING database API through a Model Context Protocol (MCP) bridge.
Installation
Install the package in development mode:
Or install from PyPI (when available):
Claude config
"mcpServers": {
"string-mcp": {
"command": "/path/to/python/env/bin/string-mcp-server",
"env": {}
}
}
}
Usage
MCP Server (Primary Use Case)
The package provides an MCP server for integration with MCP-compatible clients:
# Run the MCP server
string-mcp-server
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
Command Line Interface
The package also provides a string-mcp command 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
Python API
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)
Features
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
API Methods
Core Methods
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
Configuration
The package uses a StringConfig class 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)
Output Formats
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
Species Support
The package supports all species available in STRING. Common species IDs:
Human: 9606
Mouse: 10090
Rat: 10116
Yeast: 4932
E. coli: 511145
MCP Server Configuration
To use the MCP server with an MCP client, configure it as follows:
{
"mcpServers": {
"string-mcp": {
"command": "string-mcp-server",
"env": {}
}
}
}
The server will automatically handle:
JSON-RPC communication
Tool discovery and invocation
Error handling and reporting
Base64 encoding for image data
Development
Setup Development Environment
# 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 a tests/ directory and add test files following the pytest configuration in pyproject.toml.
Project Structure
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
License
MIT License - see LICENSE file for details.
Contributing
Fork the repository
Create a feature branch
Make your changes
Add tests
Run the test suite
Submit a pull request
Support
For issues and questions, please use the GitHub issue tracker.
Example Usage
Complete DNA Repair Protein Analysis
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.
2. Protein Identifier Mapping
Map gene symbols to STRING identifiers:
[
{
"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..."
}
]
3. Protein-Protein Interaction Network
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.
4. Network Statistics
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.
5. Functional Enrichment Analysis
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"
}
]
Disease Associations:
[
{
"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 Network
This visualization shows the protein-protein interaction network for TP53, BRCA1, BRCA2, ATM, and ATR with high-confidence interactions (score ≥ 400).
7. Functional Enrichment Visualization
The package can also create enrichment scatter plots showing the most significantly enriched biological processes.
Example Enrichment Visualization: View Functional Enrichment Plot
This visualization displays the top 10 most significantly enriched biological processes and pathways for the DNA repair protein set, showing p-values and gene counts for each enriched term.
Summary
This comprehensive analysis demonstrates that the STRING-DB MCP bridge successfully:
Identified all 5 DNA repair proteins with detailed annotations
Discovered 10 high-confidence protein interactions (all >0.8 score)
Revealed significant pathway enrichments with p-values < 1e-8
Confirmed statistical significance of the network (p = 0.0122)
Generated both network and enrichment visualizations
The results validate these proteins as a core DNA damage response module, with exceptionally strong enrichment for:
Cellular response to ionizing radiation (p = 1.52e-8)
DNA damage signaling (p = 1.32e-7)
Homologous recombination (p = 2.8e-5)
p53 signaling pathway (p = 5.44e-5)
Breast cancer associations (p = 2.02e-6)v
This showcases the complete functionality of the STRING-DB MCP bridge for protein interaction network analysis and functional annotation.
