
Claude Desktop
Desktop · Freemium · Proprietary
Anthropic's official Claude AI desktop application. Supports MCP servers to extend functionality.
Health & Wellness · Education & Learning

evangstav/personal-mcp
A Model Context Protocol server for personal health and well-being tracking. This server provides tools and resources for tracking workouts, nutrition, and daily journal entries, with AI-assisted analysis through Claude integration.
npx -y @smithery/cli install personal-mcp --client claudeLog exercises, sets, and reps
Track perceived effort and post-workout feelings
Calculate safe training weights with rehabilitation considerations
Historical workout analysis
Shoulder rehabilitation support
RPE-based load management
Log meals and individual food items
Track protein and calorie intake
Monitor hunger and satisfaction levels
Daily nutrition targets and progress
Details on this page are taken from the project's README. Open README
Clients mentioned in this server's README:

worryzyy/HowToCook-mcp
An MCP (Model Context Protocol) server based on Anduin2017/HowToCook, allowing AI assistants to recommend recipes, plan meals, and solve the age-old question of "what should I eat today?".

nexgene-research/nexonco-mcp
search_clinical_evidence: A MCP tool for querying clinical evidence data that returns formatted reports.

Kartha-AI/agentcare-mcp
Agent Care is an MCP-based server for interacting with electronic medical record systems such as Cerner and Epic, providing medical tools and FHIR data access.

christianhinge/dicom-mcp
The dicom-mcp server enables AI assistants to query, read, and move data on DICOM servers (PACS, VNA, etc.).

jmandel/health-record-mcp
This project acts as a specialized server providing tools for Large Language Models (LLMs) and other AI agents to interact with Electronic Health Records (EHRs). It leverages the SMART on FHIR standard for secure data access and the Model Context Protocol (MCP) to expose the tools.

r-huijts/strava-mcp
Connect Claude to your Strava account and ask questions in plain English: "How far did I run this month?", "Analyze my last ride", or "Show me my fastest segments.".