Ensembl API MCP Server

Overview

The Ensembl API MCP Server turns the vast, species‑rich REST interface of Ensembl into a concise set of high‑level tools that AI assistants can call directly. Instead of dealing with dozens or hundreds of individual endpoints, developers receive ten logically grouped tools—each mirroring a core biological task such as gene lookup, variant annotation, or comparative genomics. This abstraction reduces the cognitive load on AI users and streamlines workflow integration: a single tool call can fetch all relevant gene data, while another retrieves cross‑species comparisons without needing to construct complex query strings.

For developers building AI‑augmented research workflows, this server offers a production‑ready, TypeScript implementation that guarantees type safety and robust error handling. The built‑in rate‑limiting respects Ensembl’s usage policies, preventing accidental throttling during large queries. Because the tools are organized by biological concepts rather than raw REST paths, they align naturally with how scientists think about their data—making it easier to construct prompts that ask for “gene information” or “variant annotations” without wrestling with endpoint syntax.

Key capabilities include:

• Gene information and search across all Ensembl species, with support for IDs or symbols.
• Sequence retrieval for any genomic region, returning raw DNA sequences in a single call.
• Variant exploration that pulls annotations and related functional data.
• Transcript and isoform details, including exon structures and expression metrics.
• Cross‑reference navigation to external databases (e.g., UniProt, ClinVar) in a single request.
• Species‑agnostic access—the same tool set works for human, mouse, zebrafish, and many more organisms.

Real‑world scenarios benefit from this server in multiple ways: a bioinformatician can ask an AI assistant to “compare the BRCA1 gene across human and mouse” and receive a concise summary; a clinical researcher can request variant pathogenicity scores for a list of SNPs without writing any API calls; or an educator can embed gene lookup tools into interactive notebooks, letting students explore genomic data hands‑on.

Integration is straightforward: once the server is registered on a platform like Smithery or Glama, any MCP‑compliant client (Claude, Cursor, VS Code, etc.) automatically discovers the available tools. Developers can then embed tool calls into prompts or workflows, leveraging AI to orchestrate complex queries while the server handles authentication, request formatting, and response parsing. The result is a seamless bridge between natural language interaction and high‑performance genomic data retrieval, empowering researchers to focus on biology rather than boilerplate code.