"Pytest" matching MCP servers:

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 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.

MCP server for deterministic local test execution and normalized test result reporting, supporting pytest and Jest with coverage summaries.

Enables deterministic detection of pytest config-leakage that corrupts agent-benchmark grading, returning CLEAN/CORRUPTED verdicts and corrected pytest commands.

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

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.

Python MCP server for Selenium WebDriver — 84 tools for browser automation, element interactions, assertions, self-healing locators, and codegen for Java TestNG / JUnit 5 / Cucumber / pytest / C# NUnit / Playwright and CI pipelines (GitHub Actions / Jenkins / GitLab CI). No ChromeDriver setup needed.

An MCP server that runs Python project checks through pytest, Ruff, and basedpyright, with configurable settings and stdio/HTTP transport.

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.

Serves configurable fake service worlds as MCP tools with fault injection, recording every call and enabling seeded pytest assertions on final state.

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 agents to read and create feature specs, track coverage and status, and generate pytest test skeletons from specs without leaving the conversation.

Enables AI agents to investigate and repair Python/pytest repositories in isolated Git worktrees with audit trails, without modifying the original repository.

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 AI agents to build, flash, and monitor ESP-IDF projects on real hardware, including serial sessions and pytest-embedded hardware tests.

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.

Provides a code-quality backend MCP tool that automatically writes and repairs pytest tests, runs them in a sandbox, validates their effectiveness via mutation testing, and surfaces suspected source bugs for human review.