Problem solved

Bioinformatics researchers and developers often need to query multiple genomic databases using various APIs, each with its own syntax and access method. This fragmentation slows down analysis and increases the learning curve. Additionally, AI-powered assistants like Claude cannot natively interact with bioinformatics data sources. gget-mcp solves this by wrapping the gget library’s unified interface to genomic databases into a standardized MCP server. This allows AI to perform complex genomics queries directly from natural language, reducing the need for manual scripting and enabling seamless integration of bioinformatics into conversational AI workflows.

Use cases

• Retrieving gene sequences (e.g., DNA, mRNA, protein) for a given gene symbol or ID across species.
• Searching for orthologs and paralogs of a gene in different organisms to study evolutionary relationships.
• Performing BLAST searches against major databases to find similar sequences or identify unknown sequences.
• Fetching protein structures (e.g., PDB files) and associated metadata for structural biology research.
• Looking up genomic variants (e.g., SNPs) and their clinical significance from databases like Ensembl VEP or dbSNP.
• Automating the annotation of gene lists with functional information such as GO terms, pathways, and expression data.

Audience

• Developers
• Researchers
• Data Scientists

Pros

• Wraps the comprehensive gget library, providing access to numerous genomic databases through a single MCP server.
• Enables AI assistants to directly interact with bioinformatics tools, greatly expanding their utility in life sciences.
• Open-source (MIT) with a simple setup via pip, encouraging community contributions and easy integration.
• Standardized MCP interface ensures compatibility with a growing ecosystem of AI tools and frameworks.
• Includes examples and test coverage to help users understand and verify functionality.

Cons

• Dependent on external databases and the gget library; latency or downtime can affect tool availability.
• Limited documentation and relatively early stage of development; some edge cases may not be handled.
• Requires user to host and manage the MCP server locally, which may be a barrier for non-technical users.