"Optimizing AI Model Thinking, Token Usage, and Context Size" matching MCP servers:

Enables AI agents to conversationally interact with genomics research networks for data analysis and discovery across multiple Omics AI Explorer platforms. It provides tools for exploring data collections, examining table schemas, and executing SQL queries against datasets like Viral AI and Neuroscience AI.

A Model Context Protocol server that interfaces with Biomart databases, allowing models to discover biological datasets, explore attributes/filters, retrieve biological data, and translate between different biological identifiers.

An MCP server for searching and accessing RNA sequencing datasets from the European Nucleotide Archive (ENA), supporting bulk, single-cell, and spatial transcriptomics with advanced filtering and download capabilities.

Connects 3D Slicer with AI assistants through the Model Context Protocol, enabling medical image processing and scene manipulation using natural language.

Provides a Model Context Protocol server for accessing and querying biomedical data from BioThings services, including gene, variant, chemical, and taxon annotations.

Provides LLMs with structured access to critical biomedical databases including PubTator3 (PubMed/PMC), ClinicalTrials.gov, and MyVariant.info through the Model Context Protocol.

A Model Context Protocol (MCP) server that provides access to the Protein Data Bank (PDB) - the worldwide repository of information about the 3D structures of proteins, nucleic acids, and complex assemblies.

A framework that integrates Brain-Computer Interface technology with the Model Context Protocol to enable real-time neural signal processing and AI-powered interactions for healthcare, accessibility, and research applications.

A comprehensive Model Context Protocol (MCP) server for accessing the STRING protein interaction database. This server provides powerful tools for protein network analysis, functional enrichment, and comparative genomics through the STRING API.

Provides comprehensive BioPython capabilities for biological sequence analysis, alignment, database access (GenBank, UniProt, PubMed), protein structure analysis, and phylogenetics through a Model Context Protocol interface for AI-assisted bioinformatics workflows.

An MCP server that provides standardized access to biomedical knowledge bases and resources, enabling AI systems to retrieve verified information from sources like bioRxiv, EuropePMC, and various protein/gene databases.

A comprehensive Model Context Protocol (MCP) server providing advanced access to the ChEMBL chemical database.

A comprehensive Model Context Protocol server providing access to over 110 million chemical compounds with extensive molecular properties, bioassay data, and chemical informatics tools from the PubChem database.

Allows to retrieve information about an AnnData object via MCP using the read_lazy function from anndata.

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.

A comprehensive Model Context Protocol (MCP) server providing advanced access to the UniProt protein database.

A Model Context Protocol server that lets you drive PyMOL with natural language, enabling structure loading, analytical views, measurements, and protein exploration through AI assistants.

Enables building and analyzing signalling networks from interaction databases through a Model Context Protocol server.

A Model Context Protocol server that enables retrieval of -omics data from sources like PDB, ChEMBL, UniProt, PubChem, and HUGO.

Provides a natural language interface for scRNA-Seq analysis using the Scanpy library, supporting operations such as data preprocessing, clustering, and visualization. It enables AI agents and clients to perform complex single-cell transcriptomics workflows through the Model Context Protocol.

Enables Claude, Cursor, or any MCP client to query public Drosophila connectome datasets and run in-silico experiments on them locally, with every answer carrying built-in controls, uncertainty, and provenance. Currently exposes dataset listing with licence, citation, download size, and locally cached level details.

Connects PyMOL to Claude AI through the Model Context Protocol, allowing for conversational structural biology and molecular visualization through natural language commands.

A Model Context Protocol server providing programmatic access to 3D protein structural data from RCSB PDB, PDBe, and UniProt, enabling search, retrieval, comparison, and analysis of protein structures.

Enables interaction with AnnData objects via the Model Context Protocol, allowing querying and manipulation of annotated data matrices for single-cell genomics.

A Model Context Protocol server that enables AI assistants to interact with LAMMPS for molecular dynamics simulations through natural language commands.

Enables AI assistants to explore VEuPathDB WDK catalogs, searches, parameters, and gene data, and to perform gene lookups, expression summaries, step estimates, and download URL retrieval using VEuPathDB credentials.

Enables LLM agents to perform stateful genome-scale metabolic modeling with COBRApy through the Model Context Protocol, including loading models, knocking out genes, running flux balance analysis, and inspecting flux distributions.

🔍 Enable AI assistants to search and access bioRxiv papers through a simple MCP interface. The bioRxiv MCP Server provides a bridge between AI assistants and bioRxiv's preprint repository through the Model Context Protocol (MCP). It allows AI models to search for biology preprints and access their

Framework for building bioengineering automation tools that AI assistants can call via the Model Context Protocol, handling server registration and tool discovery automatically.

An MCP server that grounds protein research in the UniProt SPARQL endpoint, providing tools for querying proteins, sequences, variants, diseases, and more via intent-named tools and raw SPARQL.