"Testing Library" matching MCP servers:

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

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

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

Connects Claude Desktop to a running FreeCAD instance, enabling document management, part creation and editing, direct Python execution, parts library insertion, FEM analysis, and 3D view screenshots.

Enables AI assistants to control FreeCAD 3D modeling software, allowing creation and manipulation of 3D objects, execution of Python code, and interaction with FreeCAD's parts library through natural language.

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.

AI code generation service for KPC component library that solves hallucination problems by providing accurate component APIs, validation rules, and usage examples.

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 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.

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 allows users to run and visualize systems models using the lethain:systems library, including capabilities to run model specifications and load systems documentation into the context window.

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 real-time access to up-to-date library documentation and code examples for any programming library. Helps AI coding assistants deliver accurate, current information instead of relying on outdated training data.

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

A client library for connecting to and interacting with MCP servers hosted on smcphub.com, allowing users to find, publish, and call tools on MCP servers.

A production-ready template for creating Model Context Protocol servers with TypeScript, providing tools for efficient testing, development, and deployment.

A TypeScript-based template for building Model Context Protocol servers, featuring fast testing, automated version management, and a clean structure for MCP tool implementations.

A Model Context Protocol server that provides tools for Xcode-related operations, making it easier to work with iOS project management, building, testing, archiving, and deploying apps to both simulators and physical devices.