What is Pharmacology Mcp

pharmacology-mcp is an MCP (Model Context Protocol) server designed for the Guide to Pharmacology database, providing structured access to pharmacological data including targets, ligands, and interactions.

Use cases

Use cases for pharmacology-mcp include developing AI-driven applications for drug discovery, providing pharmacological insights in research projects, and enhancing educational tools with access to comprehensive pharmacological data.

How to use

To use pharmacology-mcp, connect to the server and utilize its API to query pharmacological data. Users can perform natural language queries to retrieve information about drug targets, ligands, interactions, and more.

Key features

Key features of pharmacology-mcp include structured access to authoritative pharmacological data sources, support for natural language queries, and type safety to ensure accurate data retrieval.

Where to use

pharmacology-mcp can be used in fields such as pharmacology, drug discovery, biomedical research, and AI applications that require access to specialized pharmacological knowledge.

pharmacology-mcp

MCP (Model Context Protocol) server for the Guide to PHARMACOLOGY database, providing access to pharmacological data including targets, ligands, and interactions.

This server implements the Model Context Protocol (MCP) for the Guide to PHARMACOLOGY, providing a standardized interface for accessing pharmacological data. MCP enables AI assistants and agents to access specialized pharmacological knowledge through structured interfaces to authoritative data sources.

The Guide to PHARMACOLOGY is an expert-curated database of drug targets and their ligands, providing comprehensive information about:

• Targets: Pharmacological targets (receptors, enzymes, ion channels, etc.)
• Ligands: Chemical compounds and drugs
• Interactions: Target-ligand interactions with affinity data
• Diseases: Disease associations
• Families: Target family classifications

If you want to understand more about what the Model Context Protocol is and how to use it more efficiently, you can take the DeepLearning AI Course or search for MCP videos on YouTube.

About MCP (Model Context Protocol)

MCP is a protocol that bridges the gap between AI systems and specialized domain knowledge. It enables:

• Structured Access: Direct connection to authoritative pharmacological data sources
• Natural Language Queries: Simplified interaction with specialized databases
• Type Safety: Strong typing and validation through FastMCP
• AI Integration: Seamless integration with AI assistants and agents

Quick Start

Installing uv

# Download and install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Verify installation
uv --version
uvx --version

uvx is a very nice tool that can run a python package installing it if needed.

Running with uvx

You can run the pharmacology-mcp server directly using uvx without cloning the repository:

# Run the server in streamed http mode (default)
uvx pharmacology-mcp

# Or explicitly specify stdio mode
uvx pharmacology-mcp stdio

# Run the server in streamable HTTP mode on default (3001) port
uvx pharmacology-mcp server

# Run on a specific port
uvx pharmacology-mcp server --port 8000

# Run the server in SSE mode
uvx pharmacology-mcp sse

In cases when there are problems with uvx often they can be caused by cleaning uv cache:

uv cache clean

The HTTP mode will start a web server that you can access at http://localhost:3001/mcp (with documentation at http://localhost:3001/docs). The STDIO mode is designed for MCP clients that communicate via standard input/output, while SSE mode uses Server-Sent Events for real-time communication.

API Endpoints

The server provides REST API endpoints for:

• Targets: Pharmacological targets (receptors, enzymes, ion channels, etc.)
• Ligands: Chemical compounds and drugs
• Interactions: Target-ligand interactions with affinity data
• Diseases: Disease associations
• Families: Target family classifications

Example API Usage

import httpx

# Search for GPCR targets
async with httpx.AsyncClient() as client:
    response = await client.post(
        "http://localhost:8000/targets",
        json={"type": "GPCR", "immuno": True}
    )
    targets = response.json()

# Get specific target information
async with httpx.AsyncClient() as client:
    response = await client.get("http://localhost:8000/targets/1")
    target = response.json()

# Search for approved drugs
async with httpx.AsyncClient() as client:
    response = await client.post(
        "http://localhost:8000/ligands",
        json={"approved": True, "type": "Synthetic organic"}
    )
    ligands = response.json()

Usage Example

Local File Tools

The server also provides MCP tools for saving search results to files:

• search_targets_to_file: Search targets and save results
• search_ligands_to_file: Search ligands and save results
• get_target_interactions_to_file: Get target interactions and save
• get_ligand_interactions_to_file: Get ligand interactions and save

Configuring your AI Client (Anthropic Claude Desktop, Cursor, Windsurf, etc.)

We provide preconfigured JSON files for different use cases:

• For STDIO mode (recommended): Use mcp-config-stdio.json
• For HTTP mode: Use mcp-config.json
• For local development: Use mcp-config-stdio-debug.json

Inspecting Pharmacology MCP server

npx @modelcontextprotocol/inspector --config mcp-config-stdio.json --server pharmacology-mcp

npx @modelcontextprotocol/inspector --config mcp-config.json --server pharmacology-mcp

npx @modelcontextprotocol/inspector --config mcp-config-stdio-debug.json --server pharmacology-mcp

npx @modelcontextprotocol/inspector

Integration with AI Systems

Simply point your AI client (like Cursor, Windsurf, ClaudeDesktop, VS Code with Copilot, or others) to use the appropriate configuration file from the repository.

Repository setup

# Clone the repository
git clone https://github.com/antonkulaga/pharmacology-mcp.git
cd pharmacology-mcp
uv sync

Running the MCP Server

If you already cloned the repo you can run the server with uv:

# Start the MCP server locally (HTTP mode)
uv run server

# Or start in STDIO mode  
uv run stdio

# Or start in SSE mode
uv run sse

Testing & Verification

Run tests for the MCP server:

uv run pytest -vvv -s

You can use MCP inspector with locally built MCP server same way as with uvx.

Note: Using the MCP Inspector is optional. Most MCP clients (like Cursor, Windsurf, etc.) will automatically display the available tools from this server once configured. However, the Inspector can be useful for detailed testing and exploration.

If you choose to use the Inspector via npx, ensure you have Node.js and npm installed. Using nvm (Node Version Manager) is recommended for managing Node.js versions.

API Documentation

When the server is running, you can access:

• Interactive API docs: http://localhost:8000/docs
• ReDoc documentation: http://localhost:8000/redoc
• OpenAPI schema: http://localhost:8000/openapi.json

Data Source

This server provides access to data from the Guide to PHARMACOLOGY, an expert-curated database of drug targets and their ligands.

License

This project is licensed under the MIT License.

• Database: Open Data Commons Open Database License (ODbL)
• Contents: Creative Commons Attribution-ShareAlike 4.0 International License

Citations

If you use this server in your research, please cite:

Armstrong JF, Faccenda E, Harding SD, Pawson AJ, Southan C, Sharman JL, Campo B, Cavanagh DR, Alexander SPH, Davenport AP, Spedding M, Davies JA; NC-IUPHAR. (2020) The IUPHAR/BPS Guide to PHARMACOLOGY in 2020: extending immunopharmacology content and introducing the IUPHAR/MMV Guide to MALARIA PHARMACOLOGY. Nucleic Acids Research 48: D1006-D1021.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgments

• Guide to PHARMACOLOGY for the comprehensive pharmacological data
• Model Context Protocol for the protocol specification
• FastMCP for the MCP server framework

This project is part of the Longevity Genie organization, which develops open-source AI assistants and libraries for health, genetics, and longevity research.