A Node.js server that integrates with pytest to facilitate the ModelContextProtocol (MCP) service tools, enabling test execution recording and environment tracking.
Enables AI assistants to run and analyze pytest tests for desktop applications through interactive commands. Supports test execution, filtering, result analysis, and debugging for comprehensive test automation workflows.
An AI-powered QA automation tool that crawls web pages, generates Playwright-based pytest tests using an LLM, runs them, and automatically heals failing tests.
mcp-test-runner is an MCP server that lets your AI client (Claude /
Cursor / Codex / Gemini) drive your entire QA loop end-to-end:
* Run tests across pytest / Jest / Cypress / Go / Maestro — single
MCP surface, one env var to switch
* Analyze a URL (Web DOM probe) or a live mobile screen (Maestro
hierarchy) to extract testable modules + candidate cases
* Generate runna
AI-powered characterization test generator that reads Python functions or class methods, synthesizes inputs, captures behavior in a sandbox, and emits pytest files to lock legacy code behavior for safe refactoring.
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.
Enables AI coding agents to debug Python projects by running pytest, extracting failure locations, displaying code context around failures, and optionally requesting fix suggestions from Gemini.
Enables AI assistants to perform comprehensive code quality checks including pylint, pytest, and mypy analysis on Python projects, with smart prompts for explaining issues and suggesting fixes.
Enables developers to check system service status, retrieve project documentation, search files, and run whitelisted commands (git, pytest, etc.) securely via natural language.
Filters verbose terminal output from commands like npm install, pip install, docker build, and pytest, reducing context token consumption for AI agents by condensing logs, removing progress bars, and grouping repeated warnings.
Wraps existing test frameworks (Jest, Vitest, Pytest) and exposes structured, LLM-optimized results via MCP tools with progressive disclosure and diff-aware execution.