"A guide to Python dependency resolution and package management" matching MCP servers:

Enables genomic sequence analysis through the Evo 2 model, supporting DNA sequence scoring, embedding, generation, and variant effect prediction with multiple model checkpoints (7B, 40B, 1B parameters).

Enables variant annotation and effect prediction using the Ensembl VEP API, with support for batch and single queries.

An MCP server for querying the KEGG bioinformatics database, providing tools, resource templates, and guided prompts for pathways, genes, compounds, and more.

Grounds gene-nomenclature work in the HUGO Gene Nomenclature Committee (HGNC) dataset, enabling resolution of gene symbols and IDs to canonical HGNC identifiers, plus cross-references and batch operations.

Enables AI agents to retrieve cited pharmacogenomic and gene-variant information, including drug–gene guidance from CPIC/DPWG and rsID lookups, so responses can quote curated sources instead of relying on memory. A local package also lets users read raw DNA files on their own computer without sending the file data to the remote service.

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 bioinformatics analysis through natural language conversations with Claude Desktop, automatically generating and executing Python scripts to produce HTML reports and visualizations.

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

Enables users to search and retrieve 3D cryo-EM and electron-tomography density maps by keyword or entry ID, returning details such as resolution, structure-determination method, sample, and release date.

Provides tools to query and resolve identifiers from the Bioregistry, including prefix metadata, CURIE resolution, and substring search.

A public, read-only MCP server and REST API that connects AI agents to Bioconductor package build, check and propagation state, release and download data, dependency and ops activity, plus searchable infrastructure documentation. Every answer is linked to the public upstream source it came from, with results trimmed to the relevant failing sections or capped lists.

Enables searching cryo-EM density maps in EMDB and raw datasets in EMPIAR, with filtering by resolution, keyword, and cross-referencing between the two databases.

Enables LLMs to run rule-based molecular interaction simulations using the Kappa language through the Kappybara package. Returns simulation results as CSV data for analysis of biochemical systems and molecular binding dynamics.

Enables Claude Code to interact with a TACC or SLURM HPC cluster for bioinformatics pipelines, allowing job management, log reading, file browsing, remote script execution, and job submission through natural language.

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.

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.

Enables AI assistants to clean PDB structures and run initial-stage energy relaxation for biomolecular modeling. It provides a structured workflow for preparing structures before production molecular dynamics or coarse-graining pipelines.

MCP server that provides tools to query harmonized gene-disease validity data from the Gene Curation Coalition, supporting consensus and conflict detection for gene-disease assertions.

Enables DNA sequence analysis and variant effect prediction using Evo2-7B via MCP tools, providing forward inference, likelihood scoring, and batch variant comparison through natural language.

MCP server exposing REINVENT4 generative molecular-design operations as validated, LLM-callable tools for drug discovery, including sampling, transfer learning, reinforcement learning, and molecular analysis.