gget MCP Server

The gget‑mcp server bridges the gap between advanced bioinformatics workflows and conversational AI assistants by exposing the full functionality of the gget library through the Model Context Protocol (MCP). For researchers and developers who need to interrogate genomic databases, perform sequence alignments, or run enrichment analyses without writing custom code, this server offers a single, type‑safe endpoint that can be called from any AI agent. The result is a natural language interface where a user can ask about gene details, mutation hotspots, or pathway associations, and the assistant will translate that request into a series of precise bioinformatics calls.

At its core, gget‑mcp implements a collection of well‑defined tools that mirror the capabilities of the underlying gget library. These include gene search and annotation, sequence retrieval (with optional translation), BLAST and BLAT searches, protein structure prediction via AlphaFold, and functional enrichment with Enrichr. Each tool is wrapped in a FastMCP handler that validates input and output types, ensuring that the data exchanged between the AI client and the server remains consistent and error‑free. This strict typing reduces the cognitive load on developers, who no longer need to handle raw API responses or craft complex query strings.

The server’s value lies in its ability to turn a conversational prompt into a multi‑step bioinformatics pipeline. For example, an assistant can first locate the Ensembl ID for a gene, fetch its sequence, run a BLAST search to identify homologs, and then predict the protein’s 3‑D structure—all while keeping the user engaged in natural language. This level of orchestration is especially useful for exploratory genomics, where researchers frequently need to iterate over different queries and quickly validate hypotheses.

Real‑world use cases span from academic research to clinical diagnostics. In translational studies, a clinician might ask an AI assistant to list all known cancer‑associated mutations for a tumor suppressor gene, and the server will return curated mutation data from COSMIC. In drug discovery, a team could request pathway enrichment for a set of candidate genes, receiving a ranked list of biological processes that inform target prioritization. Because the server exposes these tools through MCP, any AI platform—Claude, ChatGPT, or custom agents—can tap into the same bioinformatics engine without bespoke integrations.

Finally, gget‑mcp distinguishes itself by combining a mature, battle‑tested bioinformatics toolkit with the modern, agent‑friendly MCP framework. This synergy means developers can focus on building higher‑level AI experiences while relying on a robust, type‑safe backend that handles the heavy lifting of genomic data retrieval and analysis.