"Tools for Testing in Python and Java" matching MCP servers:

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

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

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

Persistent memory and pattern learning for AI coding assistants via MCP.

A full-stack API automation testing server that parses OpenAPI/Swagger/Postman/HAR specs, generates comprehensive test scenarios and executable code, and provides AI-powered review and auto-fix.

Enables AI coding assistants to browse Unreal Python API documentation, search classes and functions, and execute Python code in Unreal Editor.

An MCP server that exposes Python debugging tools backed by debugpy, providing a focused debugging surface for local scripts.

A production-grade MCP server providing a persistent Python REPL with multi-session support, sandboxing, and timeout protection, enabling LLM agents to execute Python code across multiple turns with variables that persist between calls.

Execute Python code via subprocess with timeout and error handling, returning stdout, stderr, and exit code.

A server that provides a persistent Python REPL environment through the MCP protocol, allowing execution of Python code, variable management, and package installation.

Enables secure execution of Python code in a sandboxed WebAssembly environment using Pyodide and Deno. Automatically handles package management and captures complete execution results including stdout, stderr, and return values.

Provides a persistent Python REPL session as a tool for executing code, managing files, installing packages, and initializing projects via the MCP protocol.

Executes Python code with safety constraints and manages Python packages through the Model Context Protocol.

Enables execution of Python code in a safe environment, including running scripts, installing packages, and retrieving variable values. Supports file operations and package management through pip.

Lightweight Python Notebook MCP - Enable AI assistants to create, edit, and view Jupyter notebooks via Model Context Protocol

A Model Context Protocol server that extracts and analyzes Python code structures, focusing on import/export relationships between files to help LLMs understand code context.

一个基于MCP的Python REPL服务器，支持多会话管理、代码执行、历史记录回放和包安装，为AI提供Python解释器能力。

Enables LLMs to run Python code and scripts across system and conda environments, list and install packages, and read or write text and binary files within an isolated working directory. It also provides prompt templates for generating, refactoring, and debugging Python code.

Enables LLMs to manage Python environments, execute Python code and scripts, install packages, and read or write files securely within a configured working directory.

Enables LLMs to manage Python environments, execute code and scripts, install packages, and read or write files within an isolated working directory.

Enables LLMs to interact with Python environments, run code and scripts, manage packages, and read or write files within a configured working directory.

Enables LLMs to execute Python code and scripts, manage Python environments and packages, and read or write files within an isolated working directory. It also provides prompt templates for common Python tasks such as function creation, refactoring, and debugging.

Enables LLMs to interact with local Python environments, execute code and scripts, manage packages, and read or write files within an isolated working directory.

Enables LLMs to run Python code and scripts, manage Python environments and packages, and read or write files within an isolated working directory. It also provides prompts for common Python tasks such as function creation, refactoring, and debugging.

Enables LLMs to interact with Python environments, execute code and scripts, manage packages, and read or write files within an isolated working directory.

Enables LLMs to interact with Python environments, execute code or scripts, manage packages, and read/write files within an isolated working directory.

Enables LLMs to execute Python code and scripts across system and conda environments, manage installed packages, and read or write text and binary files within an isolated working directory.

Enables LLMs to work with local Python environments by listing environments and installed packages, installing dependencies, and running code or scripts. Also supports reading, writing, and listing files within a sandboxed working directory.

Enables LLMs to manage Python environments and packages, execute Python code or scripts, and read or write files within a secured working directory. It also provides prompts for common Python tasks such as function creation, refactoring, and debugging.

Enables LLMs to interact with Python environments, execute code, manage packages, and perform file operations within a configurable working directory.

Enables LLMs to run Python code and scripts across available environments, manage packages, and read/write files within a secured working directory. It also provides prompts and resources for common Python development tasks.

Enables LLMs to execute Python code and scripts across system and conda environments, manage installed packages, and read or write text and binary files within an isolated working directory. It also exposes prompts for generating, refactoring, and debugging Python code.

Enables LLMs to interact with Python environments, execute code or scripts, manage packages, and read/write files within an isolated working directory.

Enables LLMs to manage Python environments, execute Python code and scripts, install packages, and read or write files within an isolated working directory.

Enables LLMs to inspect and use Python environments, execute code or scripts, manage installed packages, and read or write files within an isolated working directory.

Enables LLMs to execute Python code and scripts across system and conda environments, manage installed packages, and read or write text and binary files within an isolated working directory. It also exposes environment, package, and file resources plus prompt templates for creating, refactoring, and debugging Python code.

Enables LLMs to manage Python environments, execute code and scripts, install packages, and read/write files within an isolated working directory. It also provides prompts for common tasks such as function creation, refactoring, and debugging.

Enables LLMs to manage Python environments, execute code and scripts, install packages, and read or write files within an isolated working directory through MCP.

Enables LLMs to manage Python environments, execute code and scripts, install packages, and read or write files within an isolated working directory.