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STRING-MCP Server

Interact with the STRING protein-protein interaction database API.

Documentation

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:

pip install -e .

Or install from PyPI (when available):

pip install string-mcp

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

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests
  5. Run the test suite
  6. 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 👁 DNA Repair Protein 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 👁 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.