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"A tool for finding and replacing text strings in files or code" matching MCP servers:

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    A bridge connecting AI agents to NCBI's PubMed database through the Model Context Protocol, enabling seamless searching, retrieval, and analysis of biomedical literature and data.
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    Apache 2.0
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    MCP server offering verified bioinformatics tools for sequence utilities and statistics, backed by BioPython/scipy. Enables AI agents to perform accurate GC content, translation, ORF finding, motif scanning, and statistical tests through natural language.
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    MIT
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    Enables users to generate volcano plots by submitting jobs with input files and parameters, supporting local or Docker execution.
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    MCP server for querying the GWAS Catalog (EBI/NHGRI), a curated catalog of genome-wide association studies. It enables AI agents to search and retrieve study data via natural language or direct tool calls.
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    MIT
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    This server provides access to InterPro protein family, domain, and functional-site classification data from EBI. It allows querying protein annotations through natural language or direct tool calls.
    4
    MIT
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    Federates 13 gene-related MCP backends (gnomAD, GTEx, etc.) behind a single Streamable HTTP endpoint with collision-free namespacing and search-based tool discovery.
    2
    MIT
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    Enables looking up genes, fetching sequences, predicting variant consequences, finding orthologs, and cross-database xrefs via Ensembl REST API through MCP.
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    Apache 2.0
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    Provides MCP tool adapters for Bioconductor methods like limma, DESeq2, and fgsea, enabling statistical analysis of omics data through containerized R execution. It serves as a bridge between MCP clients and bioinformatics tools for reproducible research workflows.
    Apache 2.0
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    A terminal-based bioinformatics CLI chat tool that integrates Ensembl VEP, NCBI ClinVar, an MCP server layer, and OpenRouter's NVIDIA Nemotron 3 Ultra model to provide variant consequence and clinical significance lookups with clear summaries in a Rich-powered CLI.
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    Enables LLMs and AI agents to query a biomedical knowledge graph stored in RedisGraph, with tools for concept search, synonym enrichment, and study variable discovery through semantic relationships.