Awesome MCP Servers is a comprehensive collection of Model Context Protocol (MCP) servers, covering 7,158 MCP servers divided into 33 categories, including AI integration, art and media, browser automation, cloud services, databases, developer tools, file systems, finance, gaming, hardware, healthcare, infrastructure, knowledge management, location maps, marketing, monitoring, multimedia processing, operating systems, project management, scientific research, security, social media, travel and transportation, utilities, and version control, etc.

The TeslaMate MCP server is a model context protocol server that connects to the TeslaMate database, providing tools for AI assistants to query Tesla vehicle data and analysis, including functions such as vehicle information, driving statistics, charging data, battery health, and location analysis.

The Medical MCP Server is a dedicated server based on the Model Context Protocol (MCP) that provides AI assistants with access to medical data and medical information tools, including functions such as drug information query, medical literature retrieval, and health topic access.

The Druid MCP Server is a comprehensive model context protocol server designed for Apache Druid, providing rich tools, resources, and prompt templates for managing and analyzing Druid clusters. It supports multiple transmission modes, including STDIO and SSE, and provides functions such as data management, ingestion management, and monitoring and health checks.

The Kafka MCP Server is an Apache Kafka Model Context Protocol (MCP) server implemented in Go. Through a standardized interface, it enables LLM models to perform Kafka operations. It provides functions such as producing/consuming messages, managing topics and consumer groups, and monitoring cluster health. It supports multiple security authentication methods and can be integrated with AI applications such as Cursor and Claude.

The Intervals.icu MCP Server is a model context protocol service connecting Claude with the Intervals.icu API, providing authentication and data retrieval tools for obtaining activity, event, and health data.

LMStudio-MCP is a model control protocol server used to establish a communication bridge between Claude and the locally running LM Studio LLM models, enabling functions such as model health check, list retrieval, and text generation.

A multi - agent system laboratory project for educational purposes, simulating e - commerce and health consultation scenarios, demonstrating multi - agent collaboration, the use of the MCP protocol, and the integration of different LLM models.

The Oura MCP Server is a Model Context Protocol service for accessing Oura Ring data, supporting the acquisition of user health data, including multiple indicators such as sleep, activity, and stress, through personal access tokens or OAuth2 credentials.

The MedixHub MCP Server is a model context protocol service based on JavaScript, integrating medical and health API access functions, providing standardized function execution, parameter verification, and LLM integration, and supporting various medical data queries such as OpenTarget, FDA drug labels, and Monarch databases.

The medRxiv MCP Server is a bridge connecting AI assistants with the medRxiv preprint library, enabling the search and access of health science preprints through the Model Context Protocol (MCP).

Fitbit MCP is an unofficial implementation of the Model Context Protocol that allows AI assistants to access and analyze users' Fitbit health and fitness data through the Fitbit public API.

The Whoop MCP server is a Python service that provides access to the Whoop API, supporting queries for health data and interaction with language models.

The MCP Document Service is a document management system based on the Model Context Protocol, providing functions such as reading, writing, editing, searching, and health checking of Markdown documents, and supporting seamless integration with AI assistants.

The Apple Health MCP Server is a model context protocol server based on the FastMCP framework, designed for LLM agents to interact with Apple Health data. It provides a standardized interface to index and query health data through Elasticsearch, supports natural - language search, analysis, and statistical functions, includes two major toolkits for XML and Elasticsearch, and supports Docker deployment.

The Oura MCP Server is an interface service based on the Model Context Protocol, providing access to the health data of the Oura API.

This is an MCP server implemented for the VictoriaMetrics anomaly detection tool vmanomaly, which allows AI assistants to directly interact with the vmanomaly REST API through the protocol, enabling functions such as health checks, model management, configuration generation, and documentation search.

Arcanna MCP Server is a service that interacts with Arcanna AI use cases through the Model Context Protocol (MCP), providing functions such as resource management, Python coding, event query, task management, feedback system, and health monitoring.

The medRxiv MCP Server is a bridge tool connecting AI assistants and the medRxiv preprint library, enabling functions such as searching for preprints, retrieving metadata, and accessing content in the field of health science through the Model Context Protocol (MCP). It supports local storage and paper analysis prompts.

This project is the Kubernetes deployment configuration for the Model Context Protocol (MCP) service, including the Docker image building, Azure Container Registry pushing, and K8s cluster deployment processes, and supports health checks, monitoring, and security protection.

Prompt Cleaner is an MCP server based on TypeScript, providing prompt cleaning tools and health check functions. The main functions include sensitive information desensitization, structured output, client-friendly content standardization, support for configuring LLM parameters through environment variables, and following the single model strategy to ensure behavior determinacy.

A Model Context Protocol (MCP) server for monitoring and analyzing the Precision Time Protocol (PTP) system in an OpenShift cluster, providing functions such as configuration analysis, real - time log monitoring, natural language querying, and health checks.

The Theta Health MCP Server is a service that connects health data to AI assistants through the Model Context Protocol (MCP), supporting the query and management of health data such as medical records, functional examinations, and device data.

The Intervals.icu MCP Server is a model context protocol service for connecting Claude with the Intervals.icu API, providing authentication and data - acquisition tools, and supporting queries for activity, event, and health data.

The Pulsyn smart ring health data MCP server allows direct access to health indicators such as sleep tracking, heart rate variability, and activity data in AI assistants via the model context protocol.

The Senechal MCP Server is a middleware that provides health data interfaces for LLM applications. It connects the Senechal API and language models through a standardized protocol, supporting resource loading, tool calls, and prompt template functions.

The Health & Fitness Coach MCP is a comprehensive project that combines AI intelligence with traditional fitness applications. It enables intelligent interaction between AI tools and fitness data through the Model Context Protocol (MCP) and consists of two major components: a web application and an MCP server.

This project is the Kubernetes deployment configuration for the Model Context Protocol (MCP) server, including Docker image building, pushing to the Azure Container Registry, and K8s cluster deployment processes, supporting health checks, security protection, and elastic scaling.

The Suncture Healthcare MCP Server is a healthcare service tool based on the Model Context Protocol, providing functions such as medical information query, symptom analysis, and BMI calculation for AI assistants. It supports installation via npm or Docker deployment.

The Health & Fitness Coach MCP is an AI - driven comprehensive fitness tracking application, including a Web dashboard and an MCP server. It enables intelligent interaction between the AI assistant and fitness data through the Model Context Protocol, providing functions such as exercise recording, nutrition tracking, and personalized plan generation.

A Model Context Protocol server that provides access to the Whoop API, supporting queries for health data such as workout cycles, recovery status, and training loads.

The medRxiv MCP Server is a bridge connecting AI assistants to the medRxiv preprint library, providing functions for searching, obtaining metadata, and accessing content of health science preprints via the Model Context Protocol (MCP).

OMCP is an MCP server implementation based on the OMOP Common Data Model, providing a standardized interface for AI models to query and analyze healthcare data.