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STRINGmcp

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STRING DB mcp implementation

3 stars PythonOthers Updated Jul 28, 2025

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:

bash
pip install -e .

Or install from PyPI (when available):

bash
pip install string-mcp

Claude config

json
"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:

bash
# 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:

bash
# 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

python
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:

python
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:

json
{
  "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

bash
# 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

code
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:

json
[
  {
    "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:

json
[
  {
    "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:

json
{
  "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):

json
[
  {
    "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:

json
[
  {
    "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.

Frequently asked questions

What is STRINGmcp?

STRINGmcp is STRING DB mcp implementation

How do I install STRINGmcp?

Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.

Is STRINGmcp open source?

Yes — it is hosted on GitHub at https://github.com/MCPmed/STRINGmcp and has 3 stars.

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