"Using agents in VSCode to reference library documentation during programming" matching MCP servers:

Enables analysis of bulk RNA-seq data using natural language queries, executing R and Python in a Docker container with automatic sample anonymization and privacy controls.

Enables AI agents to resolve and cross-reference biological entities such as genes, proteins, and aliases across authoritative databases like HGNC, NCBI Entrez, and UniProt, with confidence scores and source citations.

An MCP server that provides tools for searching and fetching protein data from the UniProt database using the unipressed library.

Enables AI agents to search, filter, download files, and generate reports from the IGVF Data Portal using natural language commands.

Enables AI agents to resolve scientific names to Taxonomic Serial Numbers, traverse complete taxonomic lineages from kingdom to children, and retrieve vernacular names in all languages from an authoritative US-government taxonomy.

An MCP server for running local LLMs using llama-cpp-python, with built-in tools for generating drug-like molecule SMILES.

Enables AI assistants to perform DNA/RNA sequence alignment using BWA (Burrows-Wheeler Aligner), supporting both short and long read alignment to reference genomes with indexing, BWA-MEM, and BWA-backtrack algorithms.

Enables AI agents to read and manipulate networks, tables, visual styles, and more in a live Cytoscape Web session through natural language, by connecting over Chrome DevTools Protocol.

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.

An MCP server for the gget bioinformatics library that enables AI assistants to perform complex genomics queries, including gene sequence retrieval, BLAST alignments, and protein structure predictions.

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 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.

Enables AI agents to retrieve and analyze breeding data from BrAPI-compliant databases through shared tools, with human-in-the-loop approval and verification.

Enables AI assistants to perform genomic variant analysis using OakVar, including running annotation pipelines, managing 200+ annotator modules, querying variant databases, and generating reports in various formats.

A unified biomedical graph database that integrates 50+ primary data sources — genes, proteins, compounds, diseases, pathways, and clinical data — into a single queryable graph with billions of cross-reference edges. Its native MCP server gives LLMs direct access to structured, authoritative biomedical data, complementing their reasoning with reliable identifiers and up-to-date database content.

Search UniProtKB by protein function, fetch curated records, map IDs across databases, and pull reference proteomes, taxonomy, and sequences via MCP. Supports both STDIO and Streamable HTTP transports.

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.

Control PyMOL from Cursor via natural language. Enables describing molecular structures and styles in plain language to execute PyMOL commands through XML-RPC.

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.

Enables natural language interaction for scRNA-Seq analysis including preprocessing, clustering, and visualization using the CellRank library. It allows users and agents to perform complex genomic data tasks through standard MCP clients and frameworks.