"How to create draft emails using Gmail's support agent class" matching MCP servers:

Enables AI agents to rank US healthcare organizations for clinical trial site feasibility, predicting patient accrual for a Graves' disease protocol using claims and registry data.

Enables medical care management by retrieving and synthesizing PubMed literature, drafting provider emails for user review, and supporting human-in-the-loop approvals.

A zero-dependency MCP suite exposing computational tools across metabolic, cardio-renal, chronobiology, neuro-recovery, and biomechanics domains. It enables clients to derive longevity and physiology metrics such as autophagy index, glucose-ketone ratio, glycemic variability, peripheral resistance, nocturnal BP dipping, DLMO clock time, melanopic lux, caffeine clearance, EEG fatigue entropy, and grip vitality age from user-supplied measurements.

Enables compiling typed decision queries into the smallest decision-sufficient evidence context with machine-checkable Context Certificates, providing auditable executable-biology inference through local MCP tools.

Enables AI agents to discover, risk-assess, request, and track actions on validated BioPharma systems (LIMS, MES, QMS, ELN) without bypassing the validated state, routing each request through GAMP 5 risk classification, ALCOA+ checks, approval workflows, and 21 CFR Part 11 e-signatures. Agents can also retrieve signed, verifiable execution records and audit evidence for the actions they perform.

A local-first MCP server for querying multi-omic personal health data (genome, labs, wearables) with an honesty contract and progressive disclosure skills.

Enables natural-language querying of a mock legacy healthcare database and returns validated FHIR resources (Patient, Observation, Condition).

An open source biomedical research agent that provides LLMs with 28 tools for drug discovery, protein analysis, literature search, medical imaging, omics, and sandbox execution, all running locally via Ollama and MedGemma.

Enables MCP-capable AI agents to query OneAquaHealth FHIR R4 water, air and population-health data through a single standard interface, receiving a deterministic OK/CAUTION/DO_NOT_USE trust verdict, reasons and a citable FHIR reference for every value. It also exposes official OAH code definitions and supports citizen reports drafted into IG-conformant Observations, written back only after explicit human approval.

Enables secure natural language querying of a clinical SQL Server database using a local Ollama model, with strict SQL validation to protect sensitive data.

Enables analysis of clinical trial protocols using MCP tools for document listing, entity extraction, adverse event clustering, and summarization.

AI-powered HEOR (Health Economics and Outcomes Research) MCP server with 7 tools for literature search across 41 medical data sources, cost-effectiveness modeling (Markov/PartSA/PSA), and HTA dossier preparation for NICE, EMA, FDA, IQWiG, HAS, and EU JCA submissions.

Upload your raw DNA file from 23andMe, AncestryDNA, MyHeritage, FamilyTreeDNA, or any VCF -- and watch a team of AI agents fan out across 16+ public genomics databases, share discoveries with each other in real time, and produce a comprehensive health report. Everything runs on your machine. Nothing is uploaded anywhere.

Enables AI agents to search, retrieve, store, move, render, measure, and de-identify DICOM images across DIMSE and DICOMweb archives with one set of tools, including local pixel and geometry analysis.

Provides a six-tool research-assistance workflow for CRC-LNM cases using precomputed CT, pathology features, and clinical values. Enables multimodal analysis and research queries on deidentified cases.

MCP server for headless care coordination, providing tools for case-aware provider search powered by ai-carematch.

Exposes a local biomedical literature pipeline as MCP tools for automated research workflows. Enables literature search, open-access paper retrieval, and draft generation for biomedical and pathology domains through standard MCP clients.

Exposes MCP tools that compute longevity and metabolic metrics (autophagy index, GKI, MAGE), cardio-renal biomarkers (BUN/creatinine ratio, cardiac output and TPR, nocturnal BP dipping), chronobiology estimates (DLMO, melanopic lux, caffeine clearance), plus neuro-recovery and biomechanics scores. Runs as a zero-dependency Python JSON-RPC 2.0 stdio server so any MCP-compatible client can query these physiological domain calculators.

Enables computing composite glycemic load for multi-ingredient meals, modeling protein and fat attenuation of glucose spikes, and forecasting post-prandial fatigue and stable focus hours to guide meal and task scheduling.

Enables AI assistants to read COVID-19 lateral flow test results from photographs by calling a deployed computer-vision pipeline, returning verdicts like positive or negative with confidence.

Enables users to aggregate meal glycemic index/load, model protein and fat buffering, forecast post-prandial cognitive crash risk and focus hours, and receive macronutrient stabilization recommendations.

Enables modeling supplement pharmacokinetics, tracking elimination half-life and washout, evaluating competitive absorption conflicts, and generating optimized ingestion schedules.

Enables users to model two-compartment caffeine elimination, estimate adenosine A1/A2A receptor occupancy and slow-wave sleep suppression, and schedule intake windows and cutoffs from wake and bedtime.

Enables modeling of ocular light exposure, forecasting melatonin suppression delays from digital screens, and generating timed digital-sunset schedules for sleep readiness.

This MCP server enables users to model ocular light exposure and circadian melatonin onset, forecast screen-induced DLMO delays, and generate timed digital sunset schedules for light hygiene and sleep readiness.

Enables modeling cumulative sleep deficits, estimating cognitive impairment through PVT attention lapses and BAC equivalence, auditing social jetlag, and generating circadian-safe gradual sleep repayment schedules.

Provides medical symptom extraction, differential diagnosis generation, PubMed literature search, and abstract summarization tools for LLM agents.

Enables prediction of blood-brain barrier permeability for drug molecules via machine learning, with molecule name lookup, plain-language explanations, similar drug search, and applicability domain checks. Exposes these as MCP tools and orchestrates them via a LangGraph agent.

Enables AI agents to search and analyze clinical trial data from ClinicalTrials.gov using both structured SQL queries for filtering trials by status, phase, and conditions, and semantic vector search for exploring detailed protocol information like exclusion criteria.

Enables AI agents to read and analyze Dexcom CGM data through tools for current glucose, recent historical readings, and glucose statistics such as time in range.