"All About Docker" matching MCP servers:

Enables natural language queries about Swiss mandatory health insurance coverage for medications and medical devices using official BAG lists.

An MCP server that lets an LLM answer a pharmacist's question about drug shortages by normalizing drug names, finding pharmacologic alternatives, and checking their shortage status using public FDA and NLM data.

Enables MCP-compatible agents to answer open questions about NHS provider performance using live waiting-time data from RTT and My Planned Care, supplemented by CQC quality ratings.

Enables AI-assisted aquaculture manuscript writing through six specialized agents for literature review, drafting, results, abstract, copyediting, and integrity checks, all accessible from MCP-compatible chat clients.

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.

Enables AI assistants to interact with the Open Targets Platform API to search and retrieve comprehensive data about target-disease associations, drugs, genes, and drug discovery information through GraphQL queries.

An MCP server that exposes a DICOMweb-compliant DICOM archive to AI assistants. It lets any MCP-capable client search studies, series and instances, inspect metadata, read Structured and Encapsulated PDF Reports, and render image frames — all through natural language.

Enables AI assistants to rapidly gather and synthesize structural, chemical, conservation, and literature data about protein binding pockets for drug-target triage. Useful as a reconnaissance step before computational binder design.

A Model Context Protocol (MCP) server for searching the National Provider Identifier (NPI) registry. This server provides tools to search and retrieve information about healthcare providers and organizations in the United States.

Offline MCP server for ICD medical code lookup, search, and crosswalk translation with 124K codes and 102K mappings, all local with zero network calls.

This MCP is capable of analyzing medical documents and prescriptions and giving patient all the medical info it needs and answering their questions

MCP server that lets AI assistants query current, dated information about PatientNotes, an AI medical scribe, covering pricing, features, languages, compliance, and competitor comparisons.

Enables validation of clinical notes in SOAP format, checking required fields and semantic coherence using a local LLM, all processed locally with no data leaving the machine.

Bridges your Nightscout CGM data with AI assistants via the Model Context Protocol, enabling natural language queries about glucose readings, treatments, statistics, and pattern analysis.

A Model Context Protocol (MCP) server for querying drug information from the OpenFDA API. Features Retrieve drug label information by brand name Retrieve drug information by generic (active ingredient) name Get all brand versions of a generic drug Get adverse event (side effect) reports for a drug

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.

A comprehensive Model Context Protocol (MCP) server for accessing Human Protein Atlas data, providing information about protein expression, subcellular localization, pathology, and more.

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.

Provides programmatic access to ClinicalTrials.gov API with 18 specialized tools for searching, analyzing, and retrieving detailed information about 400,000+ clinical trials worldwide, including filtering by condition, location, phase, sponsor, eligibility criteria, and outcomes.

Predicts CRISPR gene dependencies and drug targets by integrating literature, regulatory/PPI networks, and genome-scale metabolic models.