# Open Targets MCP Server [![CI](https://img.shields.io/github/actions/workflow/status/nickzren/opentargets-mcp/ci.yml?label=CI)](https://github.com/nickzren/opentargets-mcp/actions/workflows/ci.yml) [![PyPI](https://img.shields.io/pypi/v/opentargets-mcp)](https://pypi.org/project/opentargets-mcp/) [![Python](https://img.shields.io/badge/Python-3.10%2B-3776AB?logo=python&logoColor=white)](https://www.python.org/) [![MCP Registry](https://img.shields.io/badge/MCP-Registry-blue)](https://registry.modelcontextprotocol.io/v0/servers?search=nickzren/opentargets&version=latest) [![Open Targets](https://img.shields.io/badge/Open%20Targets-Platform-5C85DE)](https://platform.opentargets.org/) [![License: MIT](https://img.shields.io/github/license/nickzren/opentargets-mcp)](LICENSE) 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 ### Option 1: Run once with `uvx` (no install) ```bash uvx --from git+https://github.com/nickzren/opentargets-mcp opentargets-mcp ``` ### Option 2: Claude Desktop (MCPM) ```bash # Install mcpm package manager pip install mcpm # Install the server mcpm install opentargets ``` ### Option 3: Local install (dev or self-host) ```bash git clone https://github.com/nickzren/opentargets-mcp cd opentargets-mcp pip install uv uv sync # Run (stdio transport by default) uv run python -m opentargets_mcp.server ``` ### Option 4: Docker ```bash 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. ### Claude Desktop Manual Import (optional) ```bash 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. ## 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 - **Cross-Entity Workflows**: Multi-hop tools that chain disease, target, and drug evidence for prioritization - **Raw GraphQL Power Tools**: Run single and batch raw GraphQL operations with structured status envelopes ## Why This Server This implementation is designed for practical Open Targets workflows: - **Curated breadth**: 65 curated tools plus 3 advanced GraphQL tools (68 total), spanning target, disease, drug, evidence, variant, study, metadata, and cross-entity workflow tasks. - **Safer automation**: strict ID resolution, typed parameter handling, and resilient retry behavior. - **Lower token overhead**: optional `fields` filters on core domain tools to return only what you need. - **Flexible power mode**: raw GraphQL tools are available for edge cases. ### 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 ```mermaid graph LR subgraph "Clients" A[Claude Desktop] B[Python Scripts] C[AI Agents] end subgraph "MCP Server" D[Open Targets
MCP Server] E[Tool Categories
Target • Disease • Drug
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 3.10+ with pip ## Usage ### Running the Server Standalone ```bash # Using the convenience script (installs uv if missing, then syncs dependencies) ./run.sh # Or run directly with uv (stdio transport by default) uv run python -m opentargets_mcp.server # Installed entrypoints opentargets-mcp --help # Specify transport explicitly uv run python -m opentargets_mcp.server --transport [stdio|sse|http] ``` ### Configuration - **Environment variables**: Transport/bind use `MCP_TRANSPORT`, `FASTMCP_SERVER_HOST`, and `FASTMCP_SERVER_PORT` (defaults: `stdio`, `0.0.0.0`, `8000`). API endpoint uses `OPEN_TARGETS_API_URL` (default: `https://api.platform.opentargets.org/api/v4/graphql`). For local-only development, prefer `FASTMCP_SERVER_HOST=127.0.0.1`. - **Validated settings**: environment configuration is parsed with a typed settings model at startup (`src/opentargets_mcp/settings.py`), so invalid values fail fast. - **Name resolution**: strict; unresolved names raise a clear error (use `search_entities` to find canonical IDs). - **Tool selection guidance**: the server sends a short policy to clients to prefer curated tools, use `fields` to trim output, and reserve raw GraphQL for edge cases. - **Pagination guardrails**: tool wrappers enforce `page_index >= 0`, `page_size >= 1`, and a global `page_size <= 500`. - **Command line**: `opentargets-mcp --transport [stdio|sse|http] --host 0.0.0.0 --port 8000 --api ` provides flexible transport and endpoint selection. - **Verbose logging**: add `--verbose` to elevate the global log level to DEBUG when troubleshooting. - **CLI helpers**: `--list-tools` prints all registered tools, and `--version` prints the package version. - **Rate limiting**: `OPEN_TARGETS_RATE_LIMIT_RPS` and `OPEN_TARGETS_RATE_LIMIT_BURST` can enable global server-side rate limiting. `--rate-limiting` and `OPEN_TARGETS_RATE_LIMIT_ENABLED=true` are also supported. ### Transport Modes The server supports multiple transport protocols powered by FastMCP: #### **stdio transport** (default) ```bash # For Claude Desktop (via mcpm) and local CLI tools opentargets-mcp --transport stdio ``` #### **SSE transport** ```bash # For web-based MCP clients with Server-Sent Events opentargets-mcp --transport sse --host 0.0.0.0 --port 8000 ``` #### **HTTP transport** ```bash # 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 ```bash 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: ![Open Targets React Agent Demo](docs/screenshots/react-agent-demo.png) ```bash # 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 **68** operations from the [Open Targets Platform](https://platform-docs.opentargets.org/): **65 curated tools** plus **3 advanced GraphQL tools**. Every tool returns structured JSON that mirrors the Open Targets GraphQL schema, and you can inspect the full machine-readable list with the MCP `list_tools` request. Most domain tools accept either a canonical identifier (e.g., `ENSG...`, `EFO_...`, `CHEMBL...`) or a human-readable name/symbol. When a name is provided, the server automatically resolves it to the best matching Open Targets ID. Many core tools accept an optional `fields` list (dot-paths) to filter the response payload. `search_entities` also returns `search.triples` for compact `{id, entity, name}` consumption. For edge cases, prefer curated tools + `fields` first; use raw GraphQL only when no curated tool fits. ### 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 - `get_drug_repurposing_candidates` – Multi-hop disease -> target -> drug candidate prioritization - `graphql_batch_query` – Run one GraphQL query across many variable sets ### 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 (8 tools)** — `get_disease_info`, `get_disease_associated_targets`, `get_disease_phenotypes`, `get_disease_otar_projects`, `get_disease_known_drugs`, `get_disease_ontology`, `get_disease_literature_occurrences`, `get_disease_similar_entities`. - **Drug profiling (10 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`, `get_drug_pharmacogenomics`, `get_drug_literature_occurrences`, `get_drug_similar_entities`. - **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`. - **Metadata & ontology utilities (5 tools)** — `get_api_metadata`, `get_association_datasources`, `get_gene_ontology_terms`, `get_interaction_resources`, `map_ids`. - **Workflow tools (1 tool)** — `get_drug_repurposing_candidates`. - **Batch lookups (3 tools)** — `get_targets_batch`, `get_diseases_batch`, `get_drugs_batch`. - **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`. - **Advanced GraphQL (3 tools)** — `graphql_schema`, `graphql_query`, `graphql_batch_query`. 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 Open Targets API FAQ and cache downstream where possible. ## Development ```bash # Run lint checks (same as CI/release) uv run ruff check src tests # Run tests uv run pytest tests/ -v # Inspect registered tools from CLI uv run opentargets-mcp --list-tools ```