"Information about Unit Testing" matching MCP servers:

Enables an LLM or agent to drive a VUnit HDL unit-testing project end to end: list tests, compile, run, and inspect reports and per-test logs.

Provides tools for frontend testing including code analysis, test generation, test execution, and React component testing for Jest and Cypress frameworks.

MCP server for Python build123d to help AIs develop and reason about 3D models and CAD

AI-optimized Vitest interface that provides structured test output, visual debugging context, and intelligent coverage analysis for more effective AI assistance with testing.

Control the Unity Editor from any MCP host. One package install, one click, and your AI assistant can see your scene, create objects, and talk to the console. Patina is a Rust MCP server paired with a C# Unity bridge. It connects your favorite AI coding tool directly to the Unity Editor over a local TCP channel, with zero manual config.

Enables AI assistants to write and debug integration tests by providing live access to DOM structure and executing code directly in test environments.

An MCP server that enhances sequential thinking with meta-cognitive capabilities including confidence tracking, hypothesis testing, and organized memory storage through graph-based libraries and structured JSON documents.

Enables R package development, testing, jamovi module building, and arbitrary R code execution from MCP clients like VSCode, using an RStudio Desktop integration.

A minimal reference implementation of an MCP server that responds with "Hello, World" via Streamable HTTP. Serves as a baseline for integration testing and MCP client development with production-ready features including health checks, metrics, and containerized deployment.

Provides a universal developer operations and diagnostics toolkit with sandboxed code execution, algorithm stress testing, security secret scanning, AST-based code fixes, and a visual dashboard for ERD, port/process inspection, and differential testing.

Provides secure mathematical computation capabilities including expression evaluation, symbolic math (derivatives, simplification), matrix operations, statistics, and unit conversion, with multi-tier acceleration through WebAssembly and WebWorkers for high-performance calculations.

Provides a universal bridge to interact with any OpenAI-compatible LLM API (local or cloud), enabling model testing, benchmarking, quality evaluation, and chat operations with performance metrics.

A bridge between Unity and AI assistants that enables AI to interact with Unity game environments through a standardized interface for code execution, scene analysis, and runtime debugging.

MCP Unity Server to integrate Unity Editor game engine with different AI Model clients (e.g. Claude Desktop, Windsurf, Cursor)

An MCP server that automatically optimizes AI prompts using evolutionary algorithms, helping improve prompt performance, creativity, and reliability through iterative testing and refinement.

An MCP server that exposes structured Functional Requirements Documents (FRDs) to enable AI assistants like GitHub Copilot to automatically generate complete NestJS boilerplate architectures. It orchestrates the multi-phase creation of production-ready APIs including core CRUD, database integration, JWT authentication, and unit testing.

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK. Includes example tools for calculations and greetings, plus system information resources.

An MCP server that executes tox commands to run Python tests within a project using pytest, allowing users to run all tests or specific test groups, files, cases, or directories.

Provides comprehensive documentation about AWS AgentCore framework to GenAI tools, enabling users to build production-ready AI agents with enterprise-grade security, observability, and scalability. Offers guidance on identity management, API integration, monitoring, code execution, memory storage, and tool integration for AI agents.

Facilitates unified execution and result parsing for various testing frameworks, including Bats, Pytest, Flutter, Jest, and Go, through a Model Context Protocol interface.