"A tool to manage and process a list of Swagger API endpoints" matching MCP servers:

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.

Turns any OpenAPI/Swagger specification into a functional MCP server, dynamically exposing API endpoints as LLM tools or generating standalone Python code.

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.

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.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

Provides over 1,000 creative ways to decline requests across four categories (polite, humorous, professional, and creative). The MCP server wraps a REST API to help users craft professional rejections through natural language interactions.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Enables users to turn any API's documentation into a tested MCP server whose tools expose the API's own operations, with linting, contract tests, and live verification. It accepts OpenAPI/Swagger, Postman, RAML, WSDL, GraphQL, RSS, and HTML docs.

Turns any OpenAPI REST or GraphQL API into an MCP server, allowing AI assistants like Claude to interact with APIs without writing code.

Converts any REST API endpoints into MCP tools, enabling AI clients like Cursor and Claude Desktop to call internal services directly.

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.

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

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.

Converts any REST API into MCP-compatible tools instantly by providing an OpenAPI/Swagger spec, enabling seamless integration with AI agents.

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.

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

A server that enables interaction with any API that has a Swagger/OpenAPI specification through Model Context Protocol (MCP), automatically generating tools from API endpoints and supporting multiple authentication methods.