"Using a search engine to find evidence to answer a question" 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 searching for academic papers and preprints across multiple platforms including Semantic Scholar, arXiv, PubMed, and CrossRef. It provides access to research records, DOI lookups, and journal metadata through a unified interface deployed on Cloudflare Workers.

Enables reproducible target-disease evidence dossier assembly and validation by replaying frozen Open Targets GraphQL responses and checking citations and structured assertions against deterministic scientific contracts.

Server to search PubMed (PubMed is a free, online database that allows users to search for biomedical and life sciences literature). I have created on a day MCP came out but was on vacation, I saw someone post similar server in your DB, but figured to post mine.

Enables search and retrieval of academic papers from PubMed database with advanced features like MeSH term lookup, publication statistics, and PICO-based evidence search.

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 users to search multiple public life-science data repositories (GEO, ENA, CELLxGENE, PRIDE, DataCite) with a single query, returning a unified ranked list of datasets with ontology-expanded terms and per-source error reporting.

Enables agents to search a public PubMed-derived corpus by natural language or identifiers and to retrieve publication details while traversing bounded citation and semantic literature graphs.

A stateless, deterministic Bio-Robotics Core Engine converting biological data (UniProt API) to G-code.

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 AI coding agents to search academic papers, resolve biomedical entities, mine relations, and traverse citation graphs using Semantic Scholar and PubTator3, with local caching for reproducibility.

Enables AI agents to search, filter, download files, and generate reports from the IGVF Data Portal using natural language commands.

Enables AI agents to search EBI BioSamples metadata for biological samples such as cell lines, tissues, and organisms by free-text keyword, and to fetch individual records by accession to retrieve taxId, organism, and characteristics like tissue, sex, and cell type. It can run as a hosted gateway endpoint or locally over stdio via npx.

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 AI agents to search and retrieve compounds, drugs, diseases, pathways, genes, enzymes, glycans, modules, and KO entries; fetch full parsed flat-file entries by ID; and list database contents through keyless REST tools.

MCP server for querying the GWAS Catalog (EBI/NHGRI), a curated catalog of genome-wide association studies. It enables AI agents to search and retrieve study data via natural language or direct tool calls.

Enables searching cancer genes and curated variants from CIViC, retrieving associated diseases, therapies, and graded evidence items and assertions for clinical interpretation.

Provides a natural language interface for scRNA-Seq analysis using the Scanpy library, supporting operations such as data preprocessing, clustering, and visualization. It enables AI agents and clients to perform complex single-cell transcriptomics workflows through the Model Context Protocol.

Enables AI agents to resolve genes, diseases, chemicals, variants and species to normalized ids, search ~36M PubMed abstracts and PMC full texts by free text or entity, and retrieve the machine-extracted relations between them with the supporting sentences and PMIDs. Supports auditing individual relations with full evidence passages and pulling per-article entity annotations with character offsets.