"Interacting with AI models like OpenAI or Google models" 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.

Enables language models to search biomedical literature, fetch sequences, and follow links across Entrez databases through eleven read-only tools wrapping the nine NCBI Entrez E-utilities. It handles URL building, pacing, redirects, response caps, and API-key redaction so queries can be answered without a browser or scraping.

A Model Context Protocol (MCP) server for interacting with Gen3 data commons, with GraphQL query validation to reduce hallucinations.

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.

An MCP server that provides mouse genetics data from Mouse Genome Informatics (MGI), enabling LLM agents to query markers, mutations, alleles, phenotypes, and disease models.

Enables editing and querying of Gene Ontology Causal Activity Models (GO-CAMs) through the Barista API. Supports model creation, individual and fact management, evidence addition, and causal pathway construction for biological knowledge representation.

Enables the generation, mutation, and evolution of DNA and protein sequences using various evolutionary models and phylogenetic algorithms. It supports realistic next-generation sequencing read simulation and population-level evolutionary tracking for bioinformatics research and testing.

Leverages large language models to analyze users' WeGene genetic testing reports, providing access to report data via custom URI schemes and enabling profile and report management through OAuth authentication and API utilization.

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.

Predicts CRISPR gene dependencies and drug targets by integrating literature, regulatory/PPI networks, and genome-scale metabolic models.

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.

MCP server for interacting with Galaxy bioinformatics platform, enabling AI assistants to connect to Galaxy instances, search and execute tools, manage workflows, and access other features.

Enables automated cell type annotation in scRNA-seq analysis using CellTypist models, with tools for listing, downloading, training, and annotating cell types via natural language.

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 quantum machine learning operations using Qiskit, including executing quantum circuits, computing quantum kernels, training variational quantum classifiers, and evaluating quantum ML models.

An MCP server that enables language models to fetch protein information from the UniProt database, including protein details, sequences, functions, and structures.

Reproduces the in-silico toxicological profile of Heracleum sosnowskyi metabolites from Rassabina & Fedorov (2025) using open-source models for LD50 prediction, toxicity classification, chemical space clustering, and synthesis cost estimation.

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.