"List of Servers Running as Docker Containers" matching MCP servers:

Provides seamless access to the Protein Data Bank in Europe (PDBe) API and search capabilities, enabling AI clients to query protein structures, perform advanced searches, and retrieve structural biology data.

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

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 users to search multiple public life-science data repositories (GEO, ENA, CELLxGENE, PRIDE, DataCite) with a single query, returning a unified ranked list of datasets with ontology-expanded terms and per-source error reporting.

Enables users to generate volcano plots by submitting jobs with input files and parameters, supporting local or Docker execution.

Enables workflow management and Docker image building for Bio-OS platform. Supports WDL workflow submission, validation, and monitoring, along with Docker image building and status tracking for bioinformatics workflows.

Enables AI agents to search public cancer genomics studies (TCGA, CPTAC, MSK, and more), fetch full details for individual studies, resolve gene symbols to Entrez ids, and list cancer types. Works keylessly against open cBioPortal data over a hosted MCP endpoint, a local stdio server, or plain HTTP.

Enables AI agents to search and retrieve compounds, drugs, diseases, pathways, genes, enzymes, glycans, modules, and KO entries; fetch full parsed flat-file entries by ID; and list database contents through keyless REST tools.

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

Enables querying roughly 3.2 million measured protein–ligand binding affinities from a local, read-only DuckDB build of the BindingDB MySQL dump, with tools for searching compounds and targets, retrieving potency and selectivity profiles, fetching activity records with assay and citation details, and running read-only SQL.

Enables installing, configuring, and running modular MCP servers for biology, bioinformatics, and bioimaging tasks, including local image analysis, ImageJ/Fiji bridging, and LLM-based agent workflows.

Provides a collection of MCP servers for computational chemistry tasks including molecular generation and retrosynthesis. Also offers property prediction and molecule pricing capabilities.

Enables keyless lookup of marine species and genus names, retrieval of full taxonomic lineages by AphiaID, and common vernacular names across languages via MCP tools.

Enables protein modeling and design using the Rosetta suite via Docker, including structure refinement, mutation stability analysis, docking, and loop modeling through natural language commands.

Enables AI-powered protein design, including binders, peptides, and custom proteins, with GPU-accelerated Docker deployment and async job management.

Enables protein structure prediction using the Chai-1 model via Docker, with tools for small peptides, FASTA-based predictions, MSA-enhanced predictions, batch processing, and job management.

Enables AI-powered protein structure prediction and variant analysis via Docker, with tools for submitting predictions, batch processing variants, and monitoring jobs.

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.

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.