"Developing a FHIR-Based Digital Health Application with EHR, PHR, and Phenotyping Frameworks" matching MCP servers:

Implements a secure MCP server with API Key and JWT authentication, providing tools like echo, login, secure_action, and admin_action. Includes MCP Inspector integration for testing and debugging.

Middleware that converts a serverless handler with OpenAPI spec into an MCP endpoint following stateless MCP principles.

Enables endpoint availability monitoring and HTTP health checks for single or multiple URLs.

A REST API built with FastAPI that exposes endpoints via Model Context Protocol (MCP), allowing clients to interact with CRUD operations through MCP interfaces.

Enables local analysis of OpenAPI 3 and Swagger 2 JSON/YAML spec files on disk, with no remote fetching of specs or credentials. It groups endpoints by tag, flags operations that appear to lack security, lists deprecated operations, and produces short markdown cheat sheets for agents.

Monitors API endpoint health by checking availability, measuring response times, and providing detailed status reports with error handling and timeout protection for the localhost:8080/api/ticket endpoint.

Provides AI assistants with access to OpenAPI specifications, enabling API discovery, schema retrieval, and direct API execution with support for OAuth 2.0 and other authentication methods.

Enables precise measurement of live HTTP and WebSocket services while maintaining an append-only, evidence-based ledger of claims that agents can query with explicit timestamps, staleness indicators, and instructions for re-verification.

An open standard server implementation that enables AI assistants to directly access APIs and services through Model Context Protocol, built using Cloudflare Workers for scalability.

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.

A TypeScript-based template for developing Model Context Protocol servers with features like dependency injection and service-based architecture, facilitating the creation and integration of custom data processing tools.

This server facilitates scalable discovery and execution of OpenAPI endpoints using semantic search and high-performance processing, overcoming limitations of large spec handling for streamlined API interactions.

Enables Gemini AI to interact with a FastAPI application through MCP tools for user management, task management, and dice rolling functionality. Provides natural language access to REST API endpoints including CRUD operations, health checks, and application statistics.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.

Enables AI assistants to search, analyze, and debug application API traffic captured by Tusk Drift, including HTTP requests, database queries, distributed traces, latency metrics, and error rates.

A TypeScript-based MCP server that enables testing of REST APIs through Cline. This tool allows you to test and interact with any REST API endpoints directly from your development environment.

Validates OpenAPI specs from files, inline content, or URLs, and generates prompts for agent developers based on the API analysis.

An MCP server for interacting with Treasure Data API, allowing users to retrieve database information and check server status through natural language queries.

Enables LLMs to send HTTP requests to a local development server, allowing interaction with APIs running on localhost.

Autonomous synthetic health checks, JSON-RPC 2.0 handshake validation, breaking schema drift detection, and credential leak scanning for remote MCP servers.