BioPython MCP Server

A Model Context Protocol (MCP) server that provides comprehensive BioPython capabilities for biological sequence analysis, alignment, database access, and structural bioinformatics.

Overview

BioPython MCP bridges the powerful BioPython library with MCP-enabled applications like Claude Desktop, allowing seamless integration of bioinformatics tools into AI-assisted workflows. This enables researchers, clinicians, and developers to perform complex biological analyses through natural language interfaces.

Motivation

Bioinformatics workflows often require switching between multiple tools and writing custom scripts. BioPython MCP simplifies this by:

• Unified Interface: Access BioPython's capabilities through a standardized MCP protocol

• AI Integration: Combine computational biology with AI-powered analysis and interpretation

• Workflow Automation: Chain complex bioinformatics tasks through conversational interfaces

• Accessibility: Make advanced bioinformatics tools available to non-programmers

Features

• Sequence Operations

  • DNA/RNA translation and transcription

  • Reverse complement calculation

  • GC content analysis

  • Motif finding and pattern matching

• Sequence Alignment

  • Pairwise global and local alignment

  • Multiple sequence alignment support

  • Alignment scoring with substitution matrices

• Database Access

  • GenBank sequence retrieval

  • UniProt protein data access

  • PubMed literature search

  • NCBI database queries

• Protein Structure Analysis

  • PDB structure fetching and parsing

  • Structure statistics calculation

  • Active site residue analysis

• Phylogenetics

  • Phylogenetic tree construction (NJ, UPGMA)

  • Distance matrix calculation

  • Tree visualization

Installation

Requirements

• Python 3.10 or higher

• pip or uv package manager

Quick Install with uvx (Recommended)

The fastest way to run BioPython MCP without installation:

uvx biopython-mcp

Or run from source:

git clone https://github.com/kmaneesh/biopython-mcp.git
cd biopython-mcp
uvx --from . biopython-mcp

Install from PyPI

pip install biopython-mcp

Install from Source with uv

git clone https://github.com/kmaneesh/biopython-mcp.git
cd biopython-mcp
uv venv --python 3.10
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -e ".[dev]"

Install from Source with pip

git clone https://github.com/kmaneesh/biopython-mcp.git
cd biopython-mcp
pip install -e ".[dev]"

Development Installation

For contributing or development:

git clone https://github.com/kmaneesh/biopython-mcp.git
cd biopython-mcp
uv venv --python 3.10
source .venv/bin/activate
uv pip install -e ".[dev]"
pre-commit install

Quick Start

Running the Server

Start the MCP server:

# With uvx (no installation needed)
uvx biopython-mcp

# Or if installed
biopython-mcp

Configuration for Claude Desktop

Add to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

Using uvx (recommended):

{
  "mcpServers": {
    "biopython": {
      "command": "uvx",
      "args": ["biopython-mcp"],
      "env": {
        "NCBI_EMAIL": "you@example.com",
        "NCBI_API_KEY": "your_ncbi_api_key",
        "OBSIDIAN_VAULT_PATH": "/Users/yourname/Documents/ObsidianVault"
      }
    }
  }
}

Using installed package:

{
  "mcpServers": {
    "biopython": {
      "command": "biopython-mcp",
      "env": {
        "NCBI_EMAIL": "you@example.com",
        "NCBI_API_KEY": "your_ncbi_api_key",
        "OBSIDIAN_VAULT_PATH": "/Users/yourname/Documents/ObsidianVault"
      }
    }
  }
}

Using local development version:

{
  "mcpServers": {
    "biopython": {
      "command": "uvx",
      "args": ["--from", "/path/to/biopython-mcp", "biopython-mcp"],
      "env": {
        "NCBI_EMAIL": "you@example.com",
        "NCBI_API_KEY": "your_ncbi_api_key",
        "OBSIDIAN_VAULT_PATH": "/Users/yourname/Documents/ObsidianVault"
      }
    }
  }
}

Basic Usage Example

Once configured, you can use BioPython tools through Claude Desktop:

User: Translate the DNA sequence ATGGCCATTGTAATGGGCCGC to protein

Claude: [Uses translate_sequence tool]
Result: MAIVMGR (7 amino acids)

User: What's the GC content of this sequence?

Claude: [Uses calculate_gc_content tool]
Result: 57.14% GC content

Available Tools

Sequence Operations

Alignment

Database Access

Structure Analysis

Phylogenetics

See the Tools Reference for detailed documentation.

Configuration Options

Environment Variables

• NCBI_EMAIL: Email address for NCBI Entrez queries (recommended)

• NCBI_API_KEY: API key for higher NCBI rate limits (optional)

• OBSIDIAN_VAULT_PATH: Path to your Obsidian vault root directory (optional, for pubmed_review)

  • When set, the LLM will determine the directory path and filename for saving literature reviews

  • Can be overridden per-call with the obsidian_vault parameter

Setting Environment Variables

export NCBI_EMAIL="your.email@example.com"
export NCBI_API_KEY="your_api_key_here"
export OBSIDIAN_VAULT_PATH="/Users/yourname/Documents/ObsidianVault"

Examples

Analyze a Gene Sequence

# 1. Fetch from GenBank
fetch_genbank(accession="NM_000207", email="user@example.com")

# 2. Calculate GC content
calculate_gc_content(sequence="ATGGCC...")

# 3. Translate to protein
translate_sequence(sequence="ATGGCC...")

# 4. Find start codons
find_motif(sequence="ATGGCC...", motif="ATG")

Compare Sequences

# Perform global alignment
pairwise_align(
    seq1="ATGGCCATTGTAATGGGCCGC",
    seq2="ATGGCCATTGTTATGGGCCGC",
    mode="global"
)

Build Phylogenetic Tree

# Build tree from aligned sequences
build_phylogenetic_tree(
    sequences=["ATGGCC...", "ATGGCT...", "ATGGCA..."],
    method="nj",
    labels=["Species_A", "Species_B", "Species_C"]
)

See examples/ for complete workflow examples.

Documentation

• Getting Started Guide

• Tools Reference

• Usage Examples

• API Documentation

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Development Setup

1. Fork the repository

2. Clone your fork: git clone https://github.com/yourusername/biopython-mcp.git

3. Install development dependencies: pip install -e ".[dev]"

4. Install pre-commit hooks: pre-commit install

5. Create a feature branch: git checkout -b feature-name

6. Make your changes and commit

7. Run tests: pytest

8. Push and create a pull request

Code Quality

We use:

• Black for code formatting

• Ruff for linting

• mypy for type checking

• pytest for testing

• pre-commit for automated checks

Testing

Run tests:

pytest

Run tests with coverage:

pytest --cov=biopython_mcp --cov-report=term-missing

Generate coverage reports (HTML and XML):

pytest --cov=biopython_mcp --cov-report=html --cov-report=xml --cov-report=term-missing

View HTML coverage report:

open htmlcov/index.html  # macOS
xdg-open htmlcov/index.html  # Linux
start htmlcov/index.html  # Windows

Run type checking:

mypy biopython_mcp/

CI/CD Coverage

The project uses GitHub Actions for continuous integration with automatic coverage reporting to Codecov.

Coverage is automatically uploaded when:

• Tests run on Ubuntu with Python 3.11

• Pull requests are created or updated

• Commits are pushed to main or develop branches

Note for Contributors: Coverage reports are publicly available. The CI workflow uses the CODECOV_TOKEN secret for authenticated uploads. Repository maintainers should configure this secret in GitHub repository settings.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use BioPython MCP in your research, please cite:

@software{biopython_mcp,
  title = {BioPython MCP: Model Context Protocol Server for BioPython},
  author = {BioPython MCP Contributors},
  year = {2026},
  url = {https://github.com/kmaneesh/biopython-mcp}
}

Acknowledgments

• Built on the excellent BioPython library

• Uses FastMCP for MCP implementation

• Inspired by the Model Context Protocol

Support

• Issues: GitHub Issues

• Discussions: GitHub Discussions

• Email: Contact maintainers

Roadmap

• Add support for protein secondary structure prediction

• Implement BLAST search integration

• Add sequence feature annotation tools

• Support for custom HMM profiles

• Interactive structure visualization

• Batch processing capabilities

• REST API wrapper

Related Projects

• BioPython - The core library

• Model Context Protocol - The MCP specification

• FastMCP - MCP framework for Python

Made with BioPython and MCP