"Pytest" matching MCP servers:

MCP server for debugging pytest failures, allowing users to register test failures, apply systematic debugging principles, and analyze failure patterns.

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 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.

Wraps existing test frameworks (Jest, Vitest, Pytest) and exposes structured, LLM-optimized results via MCP tools with progressive disclosure and diff-aware execution.

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.

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

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

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.

MCP server that automates the generation of production-grade async FastAPI services following Clean Architecture, with self-healing pytest-based verification and OpenAPI spec ingestion for rapid CRUD API development.

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.

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.

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.

This MCP server enables automated maintenance and code analysis for Python/pytest repositories in isolated Docker environments. It supports read-only investigations, fix-and-verify tasks, and provides full audit trails with SQLite event history and artifact exports.