"Setting Up MCP (Minecraft Control Panel or Related Tool) Helper" matching MCP servers:

Provides an MCP interface to the ROBOT command-line tool for OWL ontology editing, enabling operations like merging, reasoning, and conversion via natural language.

Enables LLM agents to search biomedical literature, retrieve article details, find related papers, manage caching, and download open-access full texts through MCP with structured, agent-friendly responses.

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.

Enables LLM-powered applications to look up bacterial and archaeal taxonomy, search genomes, browse and navigate the taxonomic tree, and compare genomes or find phylogenetic paths between taxa. It works out of the box with a built-in reference dataset covering major phyla and can be pointed at full Genome Taxonomy Database TSV files for comprehensive queries.

Enables interaction with MGnify metagenomics resources and tools through the Model Context Protocol. Provides access to MGnify's API for querying and analyzing metagenomic datasets and related biological information.

Enables AI assistants to search and retrieve prokaryotic names, taxon details, full classifications, and rank-based browsing from the List of Prokaryotic names with Standing in Nomenclature in real time, plus read related information pages.

A Model Context Protocol server that enhances language models with protein structure analysis capabilities, enabling detailed active site analysis and disease-related protein searches through established protein databases.

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.

Enables querying of the Monarch Initiative biomedical knowledge graph for genes, diseases, phenotypes, and their associations through natural language or direct tool calls.

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.

Federates 13 gene-related MCP backends (gnomAD, GTEx, etc.) behind a single Streamable HTTP endpoint with collision-free namespacing and search-based tool discovery.

Enables users to look up yeast genes/loci, search genes and alleles by free text, and retrieve Gene Ontology annotations from the Saccharomyces Genome Database.

Enables looking up genes, fetching sequences, predicting variant consequences, finding orthologs, and cross-database xrefs via Ensembl REST API through MCP.

Enables AI assistants to query VirtualFlyBrain data, run queries, and search for terms related to Drosophila neurobiology.

Validates the physical and chemical plausibility of ligand–protein docking poses using the PoseBusters tool.

Enables AI agents to look up molecular interactions from the EBI IntAct database by gene/protein name or UniProt ID, returning detection method, interaction type, organism, PubMed reference, and MI confidence score, along with fast interaction counts. It is keyless and available either through a hosted MCP endpoint, a plain HTTP API, or a local stdio server.

Look up variant allele frequencies by ancestry, gene loss-of-function constraint, gene variant lists, and sequencing coverage over gnomAD — with ClinVar significance joined in — via MCP.

MCP server for querying the EBI PRIDE Archive, a proteomics (mass-spectrometry) data repository, through natural language or direct tool calls.

Enables AI agents to resolve scientific (Latin binomial) names to Open Tree of Life taxon IDs, retrieve full taxonomic details, synonyms, and ancestor lineages, and find the most recent common ancestor of up to 10 taxa within the synthetic tree of life. Runs keylessly over a hosted gateway endpoint or locally via npx.

Enables LLMs and AI agents to search a biomedical knowledge graph for concepts and study variables, traverse semantic relationships, and look up BioData Catalyst PIC-SURE paths for cohort building.