Open Targets MCP Server

The Open Targets MCP Server acts as a lightweight gateway between AI assistants and the rich, multi‑omic data housed in the Open Targets Platform. By exposing a single GraphQL query tool, it allows developers to pull curated evidence on gene‑disease associations, drug targets, and compound interactions without writing custom API clients. This is especially valuable for researchers who need to integrate high‑quality biomedical data into AI‑driven hypothesis generation or drug‑discovery pipelines.

The server’s core functionality is a GraphQL endpoint () that accepts any valid Open Targets query. Through the tool, an AI assistant can ask for target summaries by Ensembl ID, retrieve disease associations via EFO identifiers, or explore drug‑target interactions using ChEMBL IDs. The tool’s simplicity means developers can focus on crafting the query logic within their assistant, while the MCP server handles authentication, rate‑limiting, and response formatting.

Key features include:

• Unified access to genetics, genomics, transcriptomics, and pharmacological evidence in a single query, enabling rapid cross‑layer analysis.
• Scalable transport options: a modern Streamable HTTP endpoint for reliability and an SSE fallback for legacy clients.
• Extensible toolset: although currently one GraphQL tool, the MCP architecture allows future expansion to additional endpoints or pre‑built query templates.

Real‑world use cases span early drug‑discovery workflows—such as filtering candidate targets by evidence score, generating disease‑specific target lists for virtual screening, or automating literature‑based validation of drug mechanisms. In academic settings, the server facilitates reproducible research by embedding data queries directly into AI notebooks or collaborative platforms.

Integrating this MCP server into an AI workflow is straightforward: a client (e.g., Claude Desktop) registers the server, and the assistant can invoke with a query string. The server returns JSON, which the assistant can parse and present in natural language or visualizations. This tight coupling between AI reasoning and authoritative biomedical data empowers developers to build smarter, evidence‑grounded tools without the overhead of maintaining separate API wrappers.