Pharos MCP Server

Overview of the Pharos MCP Server

The Pharos MCP Server solves a common bottleneck in biomedical AI workflows: the need to translate natural‑language queries into the specialized GraphQL API that powers the NIH’s Pharos knowledge base. By acting as a lightweight, cloud‑hosted proxy on Cloudflare Workers, it allows AI assistants such as Claude to treat Pharos like any other tool or data source. Developers can therefore embed deep, up‑to‑date druggability information directly into conversational agents without writing custom API wrappers or managing authentication tokens.

What the server does is essentially threefold. First, it receives a structured request from an AI client that follows the Model Context Protocol. Second, it converts that request into a GraphQL query that Pharos can understand, handling any necessary field mapping or parameter validation. Third, it streams the response back to the AI in a format that can be rendered as part of the conversation. This flow keeps latency low, preserves the conversational context, and allows the AI to present results in a natural, readable style.

Key capabilities of the Pharos MCP Server include:

• Protein‑centric queries that return functional annotations, disease associations, and druggability scores.
• Disease‑to‑target mapping, enabling researchers to discover which proteins are implicated in a given pathology.
• Ligand lookup that reveals approved drugs, experimental compounds, and their known activities against specific targets.
• Streaming responses that keep the AI’s user interface responsive even for complex, multi‑page queries.
• Secure, stateless operation on Cloudflare Workers, ensuring that the server scales automatically and requires no dedicated infrastructure.

Real‑world use cases are plentiful. In a drug discovery pipeline, a medicinal chemist can ask the AI to “Show me all high‑druggability targets for Parkinson’s disease” and receive a curated list of proteins with associated therapeutic compounds. In academic research, a graduate student can query “What is the role of protein X in cancer?” and get a concise summary that includes literature references. Clinical teams can also pull up “Approved drugs targeting protein Y” to inform treatment planning or repurposing studies.

Integration into existing AI workflows is seamless: once the MCP server URL is added to an assistant’s configuration, every subsequent conversation automatically gains a new tool. The assistant can invoke the Pharos skill with simple natural‑language prompts, and the MCP server handles all data retrieval behind the scenes. This removes the need for developers to write bespoke GraphQL clients, reduces maintenance overhead, and ensures that AI agents always have access to the latest druggability data from NIH.