"Exploring OpenAPI/Swagger Specifications" matching MCP servers:

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

Enables AI to scan and query local OpenAPI/Swagger documentation, listing API groups and endpoints, and generating TypeScript type definitions for specific endpoints.

A read-only MCP server for navigating OpenAPI / Swagger specifications, enabling agents to search endpoints, retrieve parameters and schemas, and inspect authentication without loading the full spec into context.

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

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.

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.

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 powerful MCP server for querying and processing large Swagger/OpenAPI JSON documents, enabling LLMs to efficiently access API documentation without loading entire files.

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 TypeScript-based MCP server that integrates with Swagger/OpenAPI specifications to expose API endpoints as tools for Large Language Models (LLMs), enabling natural language interaction with any OpenAPI-compliant API.

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.

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

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 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.

Provides seamless access to 32 Acceleronix PaaS OpenAPIs for device management, data storage, and more, with intelligent tool management and Bearer token authentication.

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.