"Health-related MCP servers or resources" matching MCP servers:

LLM routing proxy that cuts API costs 60-90% by auto-selecting the cheapest capable model across OpenAI, Anthropic, and Google. Provides stats, config, and model comparison tools via MCP.

An MCP server that securely syncs and queries your health data from Apple Health, storing it in your own Supabase Postgres database and exposing tools for AI assistants to retrieve weight, calories, macros, and more.

An MCP server for accessing India's National Digital Health Mission (NDHM) Health ID services, enabling health information management through the NDHM API.

Read-only MCP server enabling natural language querying of personal health and cultural activity scores from the health.ojimpo.com dashboard via Claude.

Provides a unified MCP interface to a personal health database, enabling AI agents to sync lifting workouts from Hevy, log food intake against a catalog, and run read-only SQL queries to cross-reference training and nutrition data.

Enables AI agents to query personal health data collected by any wearable or phone fitness app through Android Health Connect or Apple Health, exposing read-only tools for metrics like heart rate, sleep stages, VO2 max, and body composition. It can run locally over USB with nothing stored, or be self-hosted on Cloudflare Workers with D1 to retain history beyond Health Connect's 30-day limit.

Enables authenticated owners to capture, review, correct, and export bodyweight and strength-training data through a ChatGPT interface, including bounded multi-image/multi-session batch review, typed metrics with explicit provenance, pending drafts requiring guarded confirmation, and reversible deletion.

Enables AI clients to securely access, query, and mutate normalized user-controlled health data (e.g., from Apple Health or Supabase) through a bounded set of MCP tools, with optional OAuth and sandboxed deployment.

A toolkit for managing Claude desktop app extension configurations, enabling features like Brave search, file operations, Git/GitHub integration, shell access, web scraping, HTTP requests, AWS documentation search, and Obsidian note management.

Enables AI agents to manage virtualization, Active Directory, Windows hosts via WinRM, PostgreSQL databases, and shell/SSH environments through Model Context Protocol tools.

A local-first MCP server that stores nutrition, biomarker, and wearable data in SQLite and exposes it as Model Context Protocol tools, enabling MCP-aware agents to log meals, query trends, and run correlations.

Self-hosted MCP server that syncs Xiaomi fitness data to SQLite and provides authenticated tools to query health metrics (steps, sleep, HR, etc.) for AI assistants like Grok.

Open-source MCP servers for ESG data extraction, analysis, and regulation management, providing 31 tools across 6 servers for tasks like metrics extraction, PDF processing, vector storage, and web scraping.

Enables AI agents to query locally stored Samsung Health data—such as heart rate, sleep, steps, and workouts—through MCP tools for samples, daily summaries, and trend analysis while keeping everything on your own network.

Enables autonomous self-healing scraper fleet management for Bright Data Scraper Studio, with health checking, anomaly detection, and automated AI-driven scraper repair and approval.

Deploys a stateless MCP server on AWS Lambda and API Gateway using CDK, supporting Express.js or Hono implementations with bearer token authentication and MCP tools, resources, and prompts.

Exposes Fitbit sleep and recovery data to Claude via the Google Health API, with tools for sleep sessions, sleep summaries, recovery vitals, and raw health data queries.

Integrity monitor for MCP server ecosystems, providing real-time health checks, drift detection, and cascade impact analysis for any AI agent.

Provides comprehensive email deliverability and domain registration analysis, evaluating SPF, DKIM, DMARC, DNS records, and expiry to identify issues and suggest fixes.

A Model Context Protocol server that enables seamless interaction between LLM-based agents and Apple Health data, allowing users to query, analyze, and manage health records through natural language commands.