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.
Regression gate for AI-made code changes. Exposes nightward_run (captures behavior through pytest and compares it with a human-approved baseline) and nightward_status; approving changes is deliberately left to a human CLI step.
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.
Agentic Hardware-in-the-Loop (Agentic HIL) lets coding agents develop firmware on real embedded boards: flash, reset, read UART and exchange CAN traffic, diagnose and fix. Local, policy-gated MCP tools support OpenOCD, pyOCD and STM32CubeProgrammer, with pytest for CI.
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
Local, read-only MCP server for coding agents. Indexes repeated implementations in your repository (FastAPI routes, pytest fixtures, Pydantic and SQLAlchemy models) and returns source-backed examples with a bounded read plan, or a typed UNKNOWN when evidence is insufficient. No LLM or network calls.
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.
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
Wraps existing test frameworks (Jest, Vitest, Pytest) and exposes structured, LLM-optimized results via MCP tools with progressive disclosure and diff-aware execution.
Enables AI assistants to control a real browser via Selenium WebDriver for web automation, including navigation, element interaction, assertions, and recording sessions. It also generates production-ready test code in Java TestFly, Python pytest, or C# NUnit from those sessions.
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.
Agentic code-quality CLI and stdio MCP server that runs real Python and JS/TS quality engines (Ruff, pytest, ESLint, Prettier, Vitest, etc.) for linting, testing, and reviewing code.