opentargets-mcp 0.3.0

MCP server for Open Targets Platform biomedical data access

Open Targets MCP Server

A Model Context Protocol (MCP) server that exposes the Open Targets Platform GraphQL API as a set of tools for use with Claude Desktop and other MCP-compatible clients.

Quick Install

For Claude Desktop

Option 1: Using MCPM (Recommended)

# Install mcpm package manager
pip install mcpm

# Install the server
mcpm install opentargets

Option 2: Manual Setup

# Clone and setup
git clone https://github.com/nickzren/opentargets-mcp
cd opentargets-mcp
pip install uv
uv sync

# Import to Claude Desktop (stdio transport)
mcpm import stdio opentargets \
  --command "$(uv run which python)" \
  --args "-m opentargets_mcp.server --transport stdio"

Then restart Claude Desktop to start using the Open Targets tools.

For Other MCP Clients

Option 1: Manual Setup

git clone https://github.com/nickzren/opentargets-mcp
cd opentargets-mcp
pip install uv
uv sync

# Option 1: Use the simple runner script (thin wrapper around uv run)
./run.sh

# Option 2: Run directly
uv run python -m opentargets_mcp.server

Option 2: Using Docker

git clone https://github.com/nickzren/opentargets-mcp
cd opentargets-mcp

# Build and run with Docker Compose
docker-compose up -d --build

Note: the default transport is http for docker deployments.

See the configuration section below for details and how to set ports and other environment variables.

Features

Core Capabilities

• Target Analysis: Search genes/proteins by Ensembl ID or symbol. Access expression data, genetic constraints, pathways, protein interactions, safety profiles, and mouse phenotypes
• Disease Analysis: Query diseases by EFO ID. Find associated targets, phenotypes (HPO), and research projects
• Drug Discovery: Search drugs by ChEMBL ID. Access safety data, adverse events, indications, and mechanism of action
• Evidence Mining: Explore target-disease associations with scored evidence from multiple sources
• Variant Analysis: Query genetic variants, GWAS credible sets, and pharmacogenomics data
• Study Exploration: Access GWAS studies with L2G predictions and fine-mapped loci
• Smart Search: Entity resolution with synonym handling, autocomplete, and ID mapping

Data Sources

The Open Targets Platform integrates evidence from 22+ primary data sources:

• Genetics: Open Targets Genetics, ClinVar, UK Biobank, FinnGen, Gene2Phenotype, Orphanet, COSMIC
• Functional: CRISPR screens, DepMap, GeneBass
• Drugs: ChEMBL, FDA/EMA approvals, chemical probes
• Expression: GTEx, Human Protein Atlas, Expression Atlas
• Pathways: Reactome, Signor, IntAct
• Literature: Europe PMC text mining
• Safety: FAERS, pharmacogenomics data
• Models: Mouse (MGI, IMPC) phenotypes

Architecture

graph LR
    subgraph "Clients"
        A[Claude Desktop]
        B[Python Scripts]
        C[AI Agents]
    end
    
    subgraph "MCP Server"
        D[Open Targets<br/>MCP Server]
        E[Tool Categories<br/>Target • Disease • Drug<br/>Evidence • Search • Variant • Study]
    end
    
    subgraph "Open Targets"
        F[GraphQL API]
        G[22+ Data Sources]
    end
    
    A <-->|MCP Protocol| D
    B <-->|Direct API| D
    C <-->|Function Calls| D
    D <-->|GraphQL| F
    F <--> G
    
    E --> D
    
    style D fill:#e1f5fe
    style F fill:#fff3e0

The MCP server acts as a bridge between client applications and the Open Targets Platform. It translates tool calls into GraphQL queries and provides structured access to biomedical data from 22+ integrated sources.

Prerequisites

• Python 3.12+ with pip

Usage

Running the Server Standalone

# Using the convenience script (automatically handles uv setup)
./run.sh

# Or run directly with uv (stdio transport by default)
uv run python -m opentargets_mcp.server

# Specify transport explicitly
uv run python -m opentargets_mcp.server --transport [stdio|sse|http]

Configuration

• Environment variables: MCP_TRANSPORT (stdio, sse, or http), FASTMCP_SERVER_HOST, and FASTMCP_SERVER_PORT control the transport and bind address. Defaults are stdio, 0.0.0.0, and 8000. OPEN_TARGETS_API_URL can be set to use a custom Open Targets API endpoint. Default is set to the public API: https://api.platform.opentargets.org/api/v4/graphql.
• Command line: opentargets-mcp --transport [stdio|sse|http] --host 0.0.0.0 --port 8000 provides flexible transport selection.
• Verbose logging: add --verbose to elevate the global log level to DEBUG when troubleshooting.

Transport Modes

The server supports multiple transport protocols powered by FastMCP:

stdio transport (default)

# For Claude Desktop (via mcpm) and local CLI tools
opentargets-mcp --transport stdio

SSE transport

# For web-based MCP clients with Server-Sent Events
opentargets-mcp --transport sse --host 0.0.0.0 --port 8000

HTTP transport

# For streamable HTTP MCP clients
opentargets-mcp --transport http --host 0.0.0.0 --port 8000

Using with MCP Clients

• Claude Desktop: Use mcpm installation (stdio) or direct server connection (sse)
• Web MCP clients: Use SSE or HTTP transports with public URL (tunnel required)
• Custom integrations: Any transport mode depending on your client implementation

Example Scripts

uv run python examples/target_validation_profile.py EGFR
uv run python examples/disease_to_drug.py "schizophrenia"
uv run python examples/drug_safety_profile.py "osimertinib"
uv run python examples/genetic_target_prioritization.py "inflammatory bowel disease"

AI Agent Example

The ReAct Agent provides an interactive terminal interface for exploring Open Targets data:

# Copy the example .env file and add your OpenAI API key
cp .env.example .env
# Then edit .env and set your OPENAI_API_KEY

# Run agent
uv run python examples/react_agent.py

The agent uses a ReAct (Reasoning and Acting) pattern to break down complex biomedical queries into steps, making it easy to explore drug targets, diseases, and their relationships.

Available Tools

The server wraps 49 GraphQL operations from the Open Targets Platform. Every tool returns structured JSON that mirrors the official schema, and you can inspect the full machine-readable list with the MCP list_tools request.

Quick-start shortcuts

• get_target_info – Core target identity record (Ensembl IDs, synonyms, genomic coordinates)
• get_disease_info – Disease/EFO summary with therapeutic area context
• get_drug_info – ChEMBL-backed drug profile and mechanism data
• search_entities – Unified entity search with synonym handling
• get_target_associated_diseases – High-confidence target-disease links with scores
• get_disease_associated_targets – Prioritised target list for an EFO disease
• get_target_known_drugs – Approved and investigational agents for a target
• get_target_disease_evidence – Evidence details across genetics, expression, and literature

Full catalog by category

• Target identity & biology (20 tools) — get_target_info, get_target_class, get_target_alternative_genes, get_target_associated_diseases, get_target_known_drugs, get_target_literature_occurrences, get_target_expression, get_target_pathways_and_go_terms, get_target_homologues, get_target_subcellular_locations, get_target_genetic_constraint, get_target_mouse_phenotypes, get_target_hallmarks, get_target_depmap_essentiality, get_target_interactions, get_target_safety_information, get_target_tractability, get_target_chemical_probes, get_target_tep, get_target_prioritization.
• Disease analytics (4 tools) — get_disease_info, get_disease_associated_targets, get_disease_phenotypes, get_disease_otar_projects.
• Drug profiling (7 tools) — get_drug_info, get_drug_cross_references, get_drug_linked_diseases, get_drug_linked_targets, get_drug_adverse_events, get_drug_pharmacovigilance, get_drug_warnings.
• Evidence synthesis (2 tools) — get_target_disease_evidence, get_target_disease_biomarkers.
• Search & discovery (4 tools) — search_entities, search_suggestions, get_similar_targets, search_facets.
• Variant interpretation (6 tools) — get_variant_info, get_variant_credible_sets, get_variant_pharmacogenomics, get_variant_evidences, get_variant_intervals, get_variant_protein_coordinates.
• Study exploration (6 tools) — get_study_info, get_studies_by_disease, get_study_credible_sets, get_credible_set_by_id, get_credible_set_colocalisation, get_credible_sets.

Each grouping matches the data domains described in the Open Targets docs (targets, diseases, drugs, evidence, variants, and studies). For high-volume workloads, respect the platform's throttling guidance from the official API FAQ and cache downstream where possible.

Development

# Run tests
uv run pytest tests/ -v