"Resources and Tools for Working with Docker" matching MCP servers:

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

Enables MCP-capable AI clients to characterize proteins end to end: PDB/mmCIF validation, sequence extraction and typing, pLDDT confidence summaries, metal-site geometry with bidentate chelate detection, GROMACS trajectory metrics and plots, and live UniProt, AlphaFold DB, and RCSB lookups. Also ships a point-and-click Protein Lab web UI (and a persistent run registry) so students and wet-lab biologists can get the same results without any client setup.

Enables deep probabilistic analysis of single-cell omics data using scvi-tools through natural language. Supports SCVI for scRNA-seq analysis, SCANVI for cell type annotation, TOTALVI for multi-modal RNA/protein data, and PEAKVI for scATAC-seq analysis.

A specialized MCP server for Metal-Organic Framework research that provides tools for database searching, structural optimization, and energy calculations via ASE. It enables scientific workflows by allowing users to interact with MOF data and perform chemical simulations through a standard SSE interface.

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.

This server enables multi-agent conversations for interacting with Cirro's biological data platform through its OpenAPI interface, auto-generated using AG2's MCP builder.

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 interaction with MGnify metagenomics resources and tools through the Model Context Protocol. Provides access to MGnify's API for querying and analyzing metagenomic datasets and related biological information.

A comprehensive Model Context Protocol (MCP) server that provides access to the AlphaFold Protein Structure Database through a rich set of tools and resources for protein structure prediction analysis.

Exposes phylogenetic tree validation, analysis, styling, export, and Java-based rendering as 21 MCP tools, 3 resources, and 2 prompts for AI assistants. Delegates parsing and rendering to the FigTreeKit kernel, returning reproducible CLI commands and structured artifacts.

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.

Exposes a bio-hybrid neuromorphic simulation pipeline (SNN, consciousness proxies, FinalSpark NeuroPlatform) as MCP tools, resources, and prompts for AI assistants like Claude, Cursor, and VS Code.

Enables AI assistants to automate iTOL phylogenetic tree visualization workflows, including tree inspection, taxonomy and monophyly analysis, annotation template generation, batch uploads, and export through 33 tools, 3 resources, and 2 prompts.

Executes SPARQL queries against biological and biomedical RDF databases from the RDF Portal, with additional REST API integrations for bioinformatics resources.

A unified MCP server for biomedical research that connects AI systems to resources like Ensembl, EuropePMC, STRING, and more, enabling retrieval of verified domain-specific information.

Enables protein stability prediction ($DeltaDelta$G and $Delta$Tm) and systematic mutation analysis using the SPIRED-Stab deep learning model. It supports single variant analysis, batch processing, and job monitoring via Docker-based inference.

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.

A Model Control Protocol implementation for SAMtools, providing a standardized interface for working with SAM/BAM/CRAM files.

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

Enables protein sequence analysis and structure prediction by extracting ESM-2 embeddings and batch processing FASTA files via Docker. It provides tools for large-scale embedding extraction, job monitoring, and model management within an MCP-compatible environment.

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.

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 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.