UniProt MCP Server

The UniProt MCP Server bridges AI assistants with the extensive protein knowledge base of UniProt, enabling real‑time retrieval of protein function, sequence, and metadata. By exposing a lightweight MCP interface, developers can ask an assistant to fetch precise details for any UniProt accession number or perform batch queries without leaving the chat environment. This eliminates the need to manually browse UniProt’s web interface or write custom API clients, dramatically speeding up research workflows and data‑driven decision making.

At its core, the server implements two tools: for single proteins and for multiple entries. Each tool returns a structured JSON payload containing the accession, protein name, functional annotations, full amino‑acid sequence, length, and organism. The server handles common edge cases—invalid identifiers, network hiccups, and API rate limits—and provides clear error messages that the assistant can surface to users. A 24‑hour cache, backed by an ordered dictionary, reduces redundant calls and keeps response times low even under heavy load.

Key capabilities that make this server valuable include:

• Fast, batched retrieval – fetch dozens of proteins in one request, ideal for comparative studies or ontology mapping.
• Built‑in caching – 24‑hour TTL ensures that repeated queries hit local memory rather than the UniProt API, saving bandwidth and avoiding rate‑limit penalties.
• Robust error handling – the assistant can gracefully explain why a protein was not found or why a request failed, improving user trust.
• Extensibility – the MCP SDK foundation allows future expansion (e.g., adding cross‑references or variant data) without changing the assistant’s prompt structure.

Typical use cases span bioinformatics pipelines, academic literature reviews, and clinical genomics. A researcher can ask an assistant to “compare the functional motifs of proteins P04637 and P02747,” receive the full sequences, and immediately integrate them into a downstream analysis script. Clinicians can query pathogenic variants’ associated proteins to inform diagnostic reports, while developers can embed the MCP in automated data‑curation tools that pull protein annotations on demand.

Integration is straightforward: once the server is registered in an assistant’s configuration, the tools become available as part of the MCP toolset. The assistant can invoke them via natural language prompts, and the server returns structured data that can be parsed or displayed directly. This tight coupling allows developers to treat protein data as first‑class citizens in their AI workflows, enabling dynamic content generation, real‑time hypothesis testing, and seamless data enrichment—all without leaving the conversational interface.