"Swagger" matching MCP servers:

Enables natural language exploration of OpenAPI/Swagger specs, allowing users to register APIs, browse endpoints, describe schemas, and detect breaking changes through conversational queries.

swagger-mcp is a tool designed to scrape Swagger UI by extracting the swagger.json file and dynamically generating well-defined mcp tools at runtime. These tools can be utilized by the MCP client for further tool selection.

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.

Enables AI agents to search and retrieve API documentation from an internal Swagger platform via MCP.

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 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).

Automatically converts REST APIs with Swagger/OpenAPI documentation into MCP tools, making any documented API accessible to MCP-compatible clients. Supports authentication, SSL handling, and flexible configuration for seamless API integration.

A Bun-based MCP server that allows AI models to query Swagger/OpenAPI documentation from local files or remote URLs. It enables users to search for APIs, retrieve detailed endpoint definitions, and fetch schemas to facilitate code generation and API integration.

MCP server for querying Swagger/OpenAPI metadata efficiently from AI tools, enabling fast API discovery, search, and schema inspection.

Automatically converts Swagger/OpenAPI specifications into dynamic MCP tools, enabling interaction with any REST API through natural language by loading specs from local files or URLs.

A Management Control Plane server that allows users to explore and analyze Swagger/OpenAPI specifications, providing features such as endpoint exploration, schema analysis, and customizable response formatting, with support for authentication and integration with tools like Claude.

Turns any SpringDoc-powered Spring Boot API into a production-ready MCP gateway, exposing existing controllers as safe MCP tools with discovery, validation, response shaping, and workflow orchestration.

Enables progressive exploration of Swagger/OpenAPI API documentation. Supports loading multiple specs, browsing endpoints and schemas, and searching across documentation.

Parses Swagger 2.0 and OpenAPI 3.x specifications, exposing API endpoints, schemas, and authentication through MCP tools with local caching to reduce token usage.

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.

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 AI agents to dynamically discover and interact with APIs through Swagger/OpenAPI specifications and Postman collections using a strategic four-tool approach. It streamlines API integration by providing universal tools for endpoint discovery, detailed request information, and authenticated execution.

Server that ingests Swagger/OpenAPI specifications and Postman collections, providing just 4 strategic tools that allow AI agents to dynamically discover and interact with APIs instead of generating hundreds of individual tools.

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.