"How to handle REST APIs" matching MCP servers:

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Automatically converts OpenAPI/Swagger documents into MCP servers, enabling AI agents to call third-party REST APIs via the MCP protocol.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

A basic MCP server setup guide demonstrating how to configure and run Python-based MCP servers with integration examples for Bright Data web scraping and Apify Actors for product data collection.

Enables semantic search and discovery of free public APIs from an extensive catalog. Provides embedding-based search over API names and descriptions, plus detailed API information retrieval.

The API layer for AI agents. World's biggest API index with 22,000+ APIs and growing. Agents discover and call APIs at runtime with semantic search, structured metadata, and 18 Direct Call APIs including AI providers.

Enables making REST API calls to Teradata cloud services including Elastic Compute, Vector Store, OMS, and QueryGrid. Supports custom authentication, Socks5 proxy, and multipart file uploads.

A production-grade RAG server with a centralized knowledge base, offering both MCP and REST APIs for semantic search, document ingestion, and RAG-based answers.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

A service that dynamically generates MCP tools from Swagger/OpenAPI documentation, allowing Claude Desktop to directly invoke REST APIs through natural language.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Advanced content gap analysis using Query Decomposition and Keyword Fan-Out (Google's patented methodology). Tells you exactly what user queries your content covers - and what it misses. Built on academic research because I needed to understand how AI search engines actually evaluate content.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

A powerful gateway for the Model Context Protocol (MCP) that unifies AI toolchains by federating multiple MCP servers, wrapping REST APIs as MCP tools, and supporting multiple transport methods with an admin dashboard.

A Model Context Protocol server that exposes over 200+ APIs from API.market as MCP resources, allowing large language models to discover and interact with various APIs through natural language commands.

Enables AI agents to ingest documents, generate embeddings, and perform semantic search via PostgREST APIs.

A Model Context Protocol server that enables interaction with Chop's Setup system, providing an interface to retrieve setup information via a REST API.

Enables natural language querying of SQL databases and REST APIs through the MCP protocol, grounded in a semantic layer.

A high-performance Go-based MCP server that provides a microservice architecture for orchestrating diverse tools through gRPC and HTTP/REST APIs. Enables seamless integration of language-agnostic tools including ML capabilities, web search, calculations, and human interaction for intelligent agent workflows.

A high-performance FastAPI server supporting Model Context Protocol (MCP) for seamless integration with Large Language Models, featuring REST, GraphQL, and WebSocket APIs, along with real-time monitoring and vector search capabilities.