biomcp - MCP Servers Directory

BioMCP is an open source (MIT License) toolkit that empowers AI assistants and agents with specialized biomedical knowledge. Built following the Model Context Protocol (MCP), it connects AI systems to authoritative biomedical data sources, enabling them to answer questions about clinical trials, scientific literature, and genomic variants with precision and depth.

Why BioMCP?

While Large Language Models have broad general knowledge, they often lack specialized domain-specific information or access to up-to-date resources. BioMCP bridges this gap for biomedicine by:

Providing structured access to clinical trials, biomedical literature, and genomic variants
Enabling natural language queries to specialized databases without requiring knowledge of their specific syntax
Supporting biomedical research workflows through a consistent interface
Functioning as an MCP server for AI assistants and agents

Biomedical Data Sources

BioMCP integrates with three key biomedical data sources:

PubTator3/PubMed - Biomedical literature with entity annotations
ClinicalTrials.gov - Clinical trial registry and results database
MyVariant.info - Consolidated genetic variant annotation from multiple databases

Available MCP Tools

PubMed & PubTator3

article_searcher: Search for articles by genes, diseases, variants, or keywords
article_details: Get detailed article information including abstracts and full text

ClinicalTrials.gov

trial_searcher: Advanced trial search with filtering by condition, intervention, phase, etc.
trial_protocol: Detailed trial protocol information
trial_locations: Trial site locations and contact information
trial_outcomes: Results and outcome measures
trial_references: Related publications

MyVariant.info

variant_searcher: Search for genetic variants with sophisticated filtering
variant_details: Comprehensive annotations from multiple sources (CIViC, ClinVar, COSMIC, dbSNP, etc.)

Quick Start

For Claude Desktop Users

Install uv if you don't have it (recommended):
```
brew install uv

pip install uv
```
Configure Claude Desktop:
- Open Claude Desktop settings
- Navigate to Developer section
- Click "Edit Config" and add:
```
{
  "mcpServers": {
    "biomcp": {
      "command": "uv",
      "args": ["run", "--with", "biomcp-python", "biomcp", "run"]
    }
  }
}
```
- Restart Claude Desktop and start chatting about biomedical topics!

Python Package Installation

pip install biomcp-python

uv pip install biomcp-python

uv run --with biomcp-python biomcp trial search --condition "lung cancer"

Command Line Interface

BioMCP provides a comprehensive CLI for direct database interaction:

biomcp --help

biomcp run

biomcp article search --gene BRAF --disease Melanoma
biomcp article get 21717063 --full
biomcp trial search --condition "Lung Cancer" --phase PHASE3
biomcp trial get NCT04280705 Protocol
biomcp variant search --gene TP53 --significance pathogenic
biomcp variant get rs113488022

Testing & Verification

Test your BioMCP setup with the MCP Inspector:

npx @modelcontextprotocol/inspector uv run --with biomcp-python biomcp run

This opens a web interface where you can explore and test all available tools.

Enterprise Version: OncoMCP

OncoMCP extends BioMCP with GenomOncology's enterprise-grade precision oncology platform (POP), providing:

HIPAA-Compliant Deployment: Secure on-premise options
Real-Time Trial Matching: Up-to-date status and arm-level matching
Healthcare Integration: Seamless EHR and data warehouse connectivity
Curated Knowledge Base: 15,000+ trials and FDA approvals
Sophisticated Patient Matching: Using integrated clinical and molecular profiles
Advanced NLP: Structured extraction from unstructured text
Comprehensive Biomarker Processing: Mutation and rule processing

Learn more: GenomOncology

MCP Registries

Documentation

For comprehensive documentation, visit https://biomcp.org

BioMCP Examples Repo

Looking to see BioMCP in action?

Check out the companion repository: 👉 biomcp-examples

It contains real prompts, AI-generated research briefs, and evaluation runs across different models. Use it to explore capabilities, compare outputs, or benchmark your own setup.

Have a cool example of your own? We’d love for you to contribute! Just fork the repo and submit a PR with your experiment.

License

This project is licensed under the MIT License.