"How to allow an LLM to ask questions to a user" matching MCP servers:

Official public, read-only Helena Bioinformatics MCP for source-cited answers about the company and its published products.

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.

An MCP server that provides mouse genetics data from Mouse Genome Informatics (MGI), enabling LLM agents to query markers, mutations, alleles, phenotypes, and disease models.

MCP server that exposes the UniProt REST API to LLM clients, enabling search and retrieval of protein data via tools like search_uniprotkb, get_entry, and map_ids.

Enables LLM agents to search biomedical literature, retrieve article details, find related papers, manage caching, and download open-access full texts through MCP with structured, agent-friendly responses.

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.

Read-only access to a curated, evidence-labelled corpus of mTOR research: studies, entities, signed pathway relations with supporting and conflicting evidence, and open questions.

Enables LLM agents to query the CZ CELLxGENE Census single-cell atlas with ontology-aware filters, cost caps, and full provenance, allowing natural language questions about cell types, tissues, and gene expression.

Enables AI agents to resolve scientific names to Taxonomic Serial Numbers, traverse complete taxonomic lineages from kingdom to children, and retrieve vernacular names in all languages from an authoritative US-government taxonomy.

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 searching and retrieving cancer-genomics projects, cases, and genomic data files — including TCGA, TARGET, and CPTAC — via keyless, open-access metadata tools. Supports filtering by primary site, project, or data category, and can route plain-English questions to the right tool automatically.

Parses MS-DIAL binary lipidomics outputs and runs standard analyses server-side, returning compact summaries so an LLM can drive full lipidomics analysis without exposing raw matrices.

Provides programmatic access to curated SNP data by trait, enabling LLM-powered applications to search, interpret, and discover genetic variants.

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

MCP server for querying gene annotations, full-text gene search, and species taxonomy via MyGene.info. Enables AI agents to access gene data through natural language questions.

Enables AI agents to browse the Gene Ontology through the EBI QuickGO API, including keyword search for GO terms, retrieval of a term's name, aspect, definition and synonyms by id, and listing the GO annotations tied to a UniProt accession. Runs keyless over HTTP or as a local stdio server, with an optional gateway route that lets plain-English questions be answered without choosing tools manually.

An MCP server that gives LLM agents access to computational protein design tools.

An MCP server that enables language models to fetch protein information from the UniProt database, including protein details, sequences, functions, and structures.