"Resources for accessing on-chain blockchain data" matching MCP servers:

Serves read-only archived ResMed CPAP data from a local SQLite database, enabling MCP clients to query nightly summaries, raw signals, and sync status over the LAN.

A Model Context Protocol server that enables Claude to search and access detailed nutritional information from the USDA's FoodData Central database.

Provides local MCP tools to query your own Xiaomi Mi Fitness health data (sleep, workouts, heart rate, etc.) stored in a local SQLite database, enabling AI assistants to access personal health records without exposing credentials.

Enables Claude to act as a personal health coach by connecting to Garmin wearable data and Notion workspace for automated calorie tracking, photo food logging, and coaching insights.

Connects WHOOP fitness data to Poke AI assistant, enabling natural language queries for recovery scores, sleep analysis, strain tracking, and healthspan metrics.

Exposes over 90 Garmin Connect tools for tracking activities, health metrics, and training data through the Model Context Protocol. It is optimized for Poke compatibility and supports deployment to Render via HTTP.

Enables querying of current A\&E waiting times, specialist outpatient waiting times, and general outpatient clinic quotas in Hong Kong using natural language through MCP.

Enables AI agents like Claude Desktop, Cursor, and VS Code to connect directly to COLMI R02/R03 and compatible smart rings over local Bluetooth LE, exposing tools for ring scanning, battery and firmware status, activity and sleep/recovery insights, real-time heart rate and SpO2 vitals, 3-axis motion sampling, clock sync, and physical LED blinking. Runs fully locally with no vendor cloud, accounts, or subscriptions.

MCP Server for Google Cloud Healthcare API enables Agentic AI for a variety of FHIR-based digital health solutions, from smarter clinical workflows for Health Systems to Pre-Auth frameworks for Payers!

MCP server that provides access to Hong Kong government city data, including ambulance service indicators, via a FastMCP interface.

Enables Alexa+ users to coordinate household pet care by logging feeding, medication, activity, litter, and health observations for dogs and cats, with evidence-grounded triage, Q&A, and daily briefings backed by a verified veterinary library and citations.

This server enables interacting with the National Digital Health Mission's Health Information User (HIU) API, allowing agents to access and manage health information through the Multi-Agent Conversation Protocol.

Claude Code plugin and MCP server that nudges you to take outdoor breaks based on local weather, sunset timing, and session streaks. Exposes check_grass_conditions, suggest_activity, log_touch_grass, and get_stats tools — fully local, no API keys, no cloud storage.

USDA nutrition API optimized for AI agents. x402 + USDC on Base

Provides read-only access to Withings health metrics including body composition, sleep, workouts, and ECG data with local SQLite caching and trend analysis. Features incremental synchronization, automatic OAuth token refresh, and supports all 200+ Withings measurement types for comprehensive health tracking.

MCP server enabling AI agents to search and retrieve evidence reviews from evipedia.ai on health and longevity interventions, and to suggest new interventions for review.

Enables read-only recipe search and filtering on BBC Good Food by exposing available filter axes with counts, requiring no API key.

MCP server that reads glucose data from a FreeStyle sensor via the LibreLinkUp API, providing current glucose and history with explicit coverage and gaps.

Privacy-first MCP server for querying personal health and finance data with AI. Built with Python, FastMCP, and SQLite. Local-first and open source.

SuperGlookoQuery is a Claude Desktop extension (MCPB) that connects to Glooko diabetes device data and exposes it as clinical analysis tools (time in range, GMI, trends, enriched bolus/basal analysis, charting), for any pump/CGM combination Glooko supports rather than one fixed device. It's a fork of Richard Hall's podquery-mcp, reworked to discover each account's own data shape rather than assumi