"Querying a PostgreSQL database using natural language input" 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.

Enables querying celltype markers from CellMarker database using natural language.

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.

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.

MCP server that provides tools for querying the Human Phenotype Ontology (HPO) including term lookup, hierarchy exploration, cross-ontology mappings, and gene-phenotype-disease associations, all grounded in a local SQLite database for fast offline lookups.

An MCP server that provides tools for searching and fetching protein data from the UniProt database using the unipressed library.

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

Connects 3D Slicer with AI assistants through the Model Context Protocol, enabling medical image processing and scene manipulation using natural language.

Enables querying the WormBase database for C. elegans and nematode genomics data, including gene information, protein sequences, phenotypes, interactions, and expression patterns through natural language.

Enables AI-driven pharmacogenomic analysis by querying structured genetic variant, drug response, and disease risk data. Supports natural language questions about medications, traits, and health risks based on user genome data, with privacy-first local execution.

Converts messy metabolite names into standard database identifiers (KEGG, HMDB, ChEBI, PubChem, InChIKey) and performs crosswalking to Mouse-GEM for metabolic model input, with deterministic tools and an LLM reasoning layer for identity disambiguation.

Provides access to the STRING protein-protein interaction database for mapping identifiers, retrieving interaction networks, and performing functional enrichment analysis. It enables users to explore protein partners, pathways, and cross-species homology through natural language interactions.

Provides seamless access to UniProtKB protein database, enabling queries for protein entries, sequences, Gene Ontology annotations, full-text search, and ID mapping across 200+ database types.

Enables querying genomics data from the Alliance of Genome Resources across model organisms including human, mouse, rat, zebrafish, fly, worm, yeast, and xenopus. Supports gene searches, disease associations, expression data, orthologs, phenotypes, and molecular interactions through natural language.

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.

A comprehensive Model Context Protocol (MCP) server for accessing the STRING protein interaction database. This server provides powerful tools for protein network analysis, functional enrichment, and comparative genomics through the STRING API.

A Model Context Protocol server that provides tools for interacting with the STRING database to analyze protein-protein interaction networks and functional enrichment. It enables users to map protein identifiers, retrieve interaction data, and generate biological network visualizations through natural language interfaces.

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

A comprehensive Model Context Protocol (MCP) server providing advanced access to the ChEMBL chemical database.