"Understanding File Usage" matching MCP servers:

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.

Discovers, inspects, and executes requests against OpenAPI or Swagger APIs directly from documentation URLs or specification files. It enables users to trace parameter usage across an API and perform authenticated HTTP requests through a structured toolset.

Lets AI assistants inspect test run results, AI QA reports, and findings with their full evidence, and, when asked, start runs, re-run only failed tests, import API specs, or scan a website. Read-only tools are free, while work-starting tools consume the workspace's allowance and report usage limits.

A comprehensive HTTP/HTTPS tool server using curl, supporting various authentication methods, file uploads, downloads, and custom curl commands.

Enables programmatic interaction with the Canvas LMS API through the Model Context Protocol. It currently supports listing courses with enrollment type filtering and provides interactive help prompts for tool usage.

Enables a coding agent to turn an OpenAPI/Swagger file or a recorded browser session (HAR) into a curated, tested MCP server project with ordinary Python code, tests, CI, and publish workflows. It exposes tools to inspect the source, draft and redesign a workflow-level tool plan, validate it, generate the project, and verify every tool with live calls to detect API drift.

Enables AI assistants to describe, configure, and invoke structured-output LLM endpoints on the ShapeShyft platform, managing entities, projects, keys, analytics, and usage.

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.

MCP server that will proxy to your gRPC backends via reflection or descriptors file.

Optimizes MCP tool menus from OpenAPI specs by grouping operations by tag and trimming schemas to reduce token usage, improving agent tool-selection accuracy.

Enables users to expose arbitrary HTTP APIs as configurable MCP tools by defining tool specifications in a JSON configuration file.

Agent-native API client for executing REST, GraphQL, WebSocket, SSE, and gRPC requests from MCP-capable agents, with token-efficient responses and file-based collections.

Generates customized, production-grade MCP servers for Rocket.Chat by selecting only the specific API endpoints needed for a project to minimize context bloat and token usage. It uses natural language processing to parse official OpenAPI specs and automatically create optimized TypeScript servers with built-in authentication and tests.

A zero-dependency Node.js playground for debugging how MCP clients authenticate to an MCP server over HTTP.

MCP server for making API calls with authentication support. Allows configuring multiple API endpoints and performing GET, POST, PUT, DELETE requests, including form data and file uploads.

Automatically converts OpenAPI specifications into Model Context Protocol applications, enabling HTTP APIs to be managed as MCP services. It features a dynamic architecture that monitors file systems or Kubernetes ConfigMaps to update MCP tools in real-time.