"Methods and Solutions for Knowledge Storage and Management" matching MCP servers:

Provides MCP tool adapters for Bioconductor methods like limma, DESeq2, and fgsea, enabling statistical analysis of omics data through containerized R execution. It serves as a bridge between MCP clients and bioinformatics tools for reproducible research workflows.

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 editing and querying of Gene Ontology Causal Activity Models (GO-CAMs) through the Barista API. Supports model creation, individual and fact management, evidence addition, and causal pathway construction for biological knowledge representation.

Natural language-driven spatial transcriptomics analysis via MCP. Integrates 60+ methods for preprocessing, visualization, spatial statistics, cell communication, deconvolution, and trajectory analysis.

Agent-callable canine genomics API providing breed-stratified allele frequencies, pathogenicity predictions, and variant-gene-breed-disease knowledge graph for dog DNA.

Enables querying of the Monarch Initiative biomedical knowledge graph for genes, diseases, phenotypes, and their associations through natural language or direct tool calls.

Enables querying HERB 2.0's Traditional Chinese Medicine knowledge base for herbs, ingredients, gene targets, diseases, PubMed-cited papers, and GEO transcriptomic experiments, with each relationship tagged by evidence tier.

Provides access to UniProt protein sequence and function knowledge base, enabling search and retrieval of protein entries, proteomes, taxonomy, and feature annotations.

MCP server for gene family analysis, providing tools for FASTA validation and PlantCARE cis-element prediction, with backend task management for AI agents.

Enables LLMs and AI agents to search a biomedical knowledge graph for concepts and study variables, traverse semantic relationships, and look up BioData Catalyst PIC-SURE paths for cohort building.

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 querying and retrieving cell line records from the Cellosaurus knowledge base via its REST API, including searching, fetching by accession, and accessing release information.

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.

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.