MCP server for debugging pytest failures, allowing users to register test failures, apply systematic debugging principles, and analyze failure patterns.
An MCP-compliant server that enables the execution of pytest test suites and the storage of results into a QA platform database. It allows AI models to trigger test runs, track execution progress, and retrieve historical test data through specialized tool interfaces.
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.
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
MCP server for API test case generation from Swagger/OpenAPI specs. Parses Swagger 2.0 and OpenAPI 3.x, generates test cases across 8 categories (positive, negative, boundary, auth, security, idempotency, pagination, business logic), and exports to Postman, TestRail, Allure, k6, pytest, Gherkin, and CSV. Supports internal corporate APIs with auth headers. Auto-saves export files to your working di
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.
Facilitates unified execution and result parsing for various testing frameworks, including Bats, Pytest, Flutter, Jest, and Go, through a Model Context Protocol interface.
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.
Connect AI agents to your test results, insights, and targets. Query test runs, failures, flaky tests, and regressions across frameworks including Playwright, Jest, Pytest, Cypress and more.
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.
An intelligent continuous integration server that provides automated Python code analysis, linting, and static type checking through the Model Context Protocol. It enables AI assistants to perform quality assessments and run tests using tools like pylint, mypy, and pytest.
Enables developers to check system service status, retrieve project documentation, search files, and run whitelisted commands (git, pytest, etc.) securely via natural language.