"Exploring OpenAPI/Swagger Specifications" matching MCP servers:

Converts any Swagger/OpenAPI specification into an MCP server, enabling AI assistants to intelligently query API endpoints, schemas, and generate code examples.

MCP server that provides tools for exploring and testing APIs through Swagger/OpenAPI documentation.

A Model Context Protocol server that enables LLMs to explore and interact with API specifications by providing tools for loading, browsing, and getting detailed information about API endpoints.

MCP server for loading and exploring OpenAPI/Swagger specifications, enabling AI assistants to dynamically browse API contracts by loading specs, searching endpoints, inspecting schemas, and retrieving operations.

Loads and queries OpenAPI/Swagger documents, providing tools to list APIs, get details, search endpoints, and manage schemas for efficient API exploration.

High-performance server for exploring Swagger/OpenAPI specifications with dynamic session management, lightning-fast endpoint search, and efficient caching. Enables AI assistants to discover, search, and generate code from REST APIs.

Automatically converts Swagger/OpenAPI specifications into MCP servers, enabling AI agents to interact with any REST API through natural language by exposing endpoints as AI-friendly tools.

MCP server for API test case generation from Swagger/OpenAPI specs. Parses Swagger 2.0 and OpenAPI 3.x, generates test cases across 8 categories (positive, negative, boundary, auth, security, idempotency, pagination, business logic), and exports to Postman, TestRail, Allure, k6, pytest, Gherkin, and CSV. Supports internal corporate APIs with auth headers. Auto-saves export files to your working di

A Swagger/OpenAPI query tool for MCP clients like Cursor, enabling AI assistants to browse, search, and retrieve detailed API type information.

An MCP server that connects to a Swagger specification and helps an AI to build all the required models to generate a MCP server for that service.

A stdio MCP server that caches Swagger/OpenAPI documents for multiple backend services and provides offline API search, retrieval, and request example generation. It only contacts remote servers when explicitly refreshing the cache.

Enables reading and parsing Swagger/OpenAPI specifications to list API endpoints, get detailed endpoint info, search APIs, and generate TypeScript types for request/response.

Automatically converts Swagger 2.0 API specifications into MCP tools, enabling AI assistants to directly call REST APIs. Supports both local Swagger files and remote URLs with comprehensive parameter validation and error handling.

An MCP server that allows users to query and retrieve request and response JSON schemas directly from Swagger/OpenAPI specifications. It supports automatic reference resolution and path parameter matching to help AI models interact with API interfaces.

An MCP server that crawls and parses OpenAPI JSON or Swagger UI URLs to provide concise endpoint summaries. It enables LLMs to discover and interact with API interfaces by extracting methods, paths, and operation IDs from documentation sources.

A server based on Model Context Protocol that parses Swagger/OpenAPI documents and generates TypeScript types and API client code for different frameworks (Axios, Fetch, React Query).

Enables AI assistants to discover, search, and interact with REST APIs by parsing OpenAPI/Swagger specifications with intelligent fuzzy search across endpoints, supporting both local and remote API sources.

Enables users to convert any OpenAPI or Swagger spec URL into a hosted MCP server on the Agentic Tools Platform, with tools for analyzing, trimming, and managing OpenAPI specifications.

Enables AI agents to discover, search, and call any REST API described by an OpenAPI or Swagger document. Supports multiple API endpoints with authentication and parameter handling.

Enables AI to interact with multiple Swagger/OpenAPI specifications by listing endpoints, exploring models, and generating TypeScript code. It centralizes the management of various APIs through a configuration file, allowing for seamless integration of documentation into AI workflows.