"JetBrains YouTrack issue and project tracking tool" matching MCP servers:

MCP server providing diagnostic tools to analyze software architecture, security, REST API compliance, and dependencies for multiple programming languages. Enables AI agents to run scans and audits on codebases.

Manages project standards, configurations, and API debugging for AI-assisted development, ensuring unified development practices across teams and machines.

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.

An MCP server that generates AI agent tools from Postman collections and requests. This server integrates with the Postman API to convert API endpoints into type-safe code that can be used with various AI frameworks.

An intelligent MCP server for QA automation that enables AI agents to perform stateful API testing by converting Postman collections into MCP tools, with automatic authentication, session-based state management, and transparent error handling.

A versatile tool that converts Web API interfaces into MCP tools for AI assistants, allowing them to access various web services through simple configuration and API key management.

This server facilitates scalable discovery and execution of OpenAPI endpoints using semantic search and high-performance processing, overcoming limitations of large spec handling for streamlined API interactions.

An MCP server that integrates EVE Online's ESI API with AI assistants to enable natural language interaction with character data, market information, and assets. It provides 22 tools for managing skills, wallet balances, ship fittings, and more directly through AI-powered clients.

A Python-based MCP server that dynamically fetches plugin definitions from SuperiorAPIs and auto-generates tool functions based on OpenAPI schemas, enabling seamless integration with API services.

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.

A TypeScript-based MCP server that enables testing of REST APIs through Cline. This tool allows you to test and interact with any REST API endpoints directly from your development environment.

Provides a generic HTTP client tool allowing AI agents to make arbitrary HTTP requests from their environment, with support for environment-variable placeholders for secrets.

Enables testing how different MCP clients handle tool results containing both content and structuredContent, including outputSchema enforcement and mismatch scenarios.

A TypeScript-based MCP server that enables AI assistants to execute HTTP requests with full control over methods, headers, and body content. It facilitates seamless interaction with web APIs and endpoints through a versatile tool that handles automatic JSON formatting and response truncation.

A Model Context Protocol (MCP) server for authenticated REST APIs. Drop it into any project, point it at your API, and let AI agents call endpoints — with auto-login, 2FA support, Swagger spec fetch, and fuzzy endpoint search — all without writing a single line of auth code.

A generic MCP (Model Context Protocol) server that bridges any GraphQL API to Claude Code. It introspects your GraphQL schema and exposes each query and mutation as an individual tool, letting Claude interact with your API directly.

Universal HTTP client tool for LLM agents. Enables Claude and other MCP-compatible agents to perform GET, POST, PUT, DELETE, and other HTTP requests to any URL, including localhost and internal network addresses.

Turn any OpenAPI spec into MCP tools for Claude — instantly. Point mcp-openapi at any OpenAPI 3.x spec and Claude can call every endpoint through natural language. No custom integration code. No manual tool definitions. One line of config.

Enables searching YApi interfaces by keyword and retrieving detailed interface schemas, including request/response, for project-level tokens.

Converts natural language queries into valid GraphQL queries and executes them against GraphQL APIs. Includes schema introspection, query validation, execution with authentication, and query history tracking.