Ensembl Mcp Server

The Unofficial Ensembl MCP Server bridges the gap between AI assistants and one of biology’s most comprehensive genomic resources. By exposing Ensembl’s RESTful endpoints through the Model Context Protocol, it allows developers to embed real‑time gene, sequence, variant, and comparative genomics data directly into conversational agents. This eliminates the need for custom wrappers or manual API integration, enabling rapid prototyping of biology‑aware assistants that can answer complex queries about genes, proteins, and evolutionary relationships.

At its core, the server translates standard Ensembl requests into MCP tools that Claude (or any MCP‑compatible client) can invoke. For example, a user might ask the assistant to “show me all transcripts for BRCA1 in humans,” and the server will return a structured transcript list, including exon coordinates and biotype information. The same mechanism supports sequence extraction, variant lookup in a genomic window, or retrieval of regulatory elements such as enhancers and transcription‑factor binding sites. Each tool returns JSON objects that can be rendered or further processed by the assistant, ensuring consistency across different genomic queries.

Key capabilities include gene lookup and search, comprehensive transcript retrieval, DNA sequence extraction (with optional CDS or repeat‑masked variants), variant consequence prediction, and cross‑species homology detection. The server also offers phylogenetic tree generation in multiple formats, ontology term access (GO), coordinate mapping between assemblies, and batch processing for high‑throughput workflows. These features are packaged as discrete MCP tools with clear, self‑describing schemas, making them easy to discover and compose within an AI workflow.

Real‑world scenarios range from academic research assistants that pull up gene annotations on demand, to clinical decision support systems that fetch patient‑specific variant effects from Ensembl’s population genetics data. Bioinformatics pipelines can also leverage the server to annotate sequencing results on the fly, while educational tools can provide instant visualizations of gene structure or evolutionary relationships during interactive lessons. Because the server operates over a standard protocol, it can be integrated into any MCP‑enabled environment—Claude Desktop, web chatbots, or even custom voice assistants.

What sets this MCP server apart is its breadth of coverage and the ease with which it can be added to an existing AI workflow. Rather than writing bespoke API calls for each Ensembl endpoint, developers can simply reference the available tools in their prompts or code. The server’s batch operations reduce network overhead, and its support for coordinate conversion ensures compatibility across different genome assemblies. By providing a single point of access to Ensembl’s vast dataset, the Unofficial Ensembl MCP Server empowers developers to build richer, data‑driven AI experiences that are both scientifically accurate and developer‑friendly.