"How to run Docker and view logs" 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 assistants to run reproducible bioinformatics pipelines over MCP, with verifiable provenance via checksums and Workflow Run RO-Crate metadata.

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.

Bridge any AI coding assistant directly to GenePattern — run bioinformatics modules, manage cloud jobs, and stream results, all through natural language.

Exposes pipen bioinformatics pipelines as MCP tools, allowing AI assistants to discover and run complex workflows through a progressive disclosure interface.

Enables LLM agents to design proteins, predict structures, score interfaces, and run molecular dynamics simulations through a unified interface.

MCP server that exposes STRING database functionality, allowing AI agents to resolve protein identifiers, retrieve interaction networks, perform homology lookups, and run functional enrichment analysis.

Enables AI assistants to query VirtualFlyBrain data, run queries, and search for terms related to Drosophila neurobiology.

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.

BioOpenMCP enables users to run bioinformatics tools like FastQC, Cutadapt, and STAR with background execution and status checking. It integrates with Claude Desktop to perform quality control, trimming, alignment, and reporting via natural language.

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.

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.

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 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.