"Java command reference and usage" matching MCP servers:

Enables LLMs to make HTTP requests with OAuth2, session cookies, retry logic, and cURL command generation, while providing security features like SSRF protection and TLS enforcement.

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 MCP hosts to list, look up, and describe a static catalog of OpenAPI operations, and to inspect a governing contract and explicit deny-list that define what the server refuses to do.

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.

Provides a reference MCP server that implements every MCP feature so client, gateway, and runtime authors can test against a single endpoint, and publishes a service contract describing its capabilities.

A configurable MCP server that adapts any HTTP API into an MCP toolset with generic HTTP tools (GET, POST, PUT, DELETE) and pluggable authentication. Includes API discovery scripts and supports dynamic tool generation from OpenAPI specs or wordlist scans.

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.

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.

Implements a secure MCP server with API Key and JWT authentication, providing tools like echo, login, secure_action, and admin_action. Includes MCP Inspector integration for testing and debugging.

Integrate Claude with Any OpenAI SDK Compatible Chat Completion API - OpenAI, Perplexity, Groq, xAI, PyroPrompts and more.

A Python-based MCP server that integrates OpenAPI-described REST APIs into MCP workflows, enabling dynamic exposure of API endpoints as MCP tools.

Exposes add, subtract, multiply, and divide tools for performing arithmetic operations over stdio via the Model Context Protocol.

Exposes FastAPI routes as MCP tools with auto-discovery, Pydantic schema extraction, and built-in observability.

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

Enables precise measurement of live HTTP and WebSocket services while maintaining an append-only, evidence-based ledger of claims that agents can query with explicit timestamps, staleness indicators, and instructions for re-verification.

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.

A TypeScript-based MCP server that provides backend API handling and facilitates communication between microservices. Features an organized structure with controllers, routes, and models for easy extensibility and maintenance.

Enables LLMs to interact directly with generated REST APIs by exposing CRUD actions as MCP tools and read-only data as MCP resources.

Turns any curl command into an MCP tool server by extracting placeholders and serving the request over stdio.