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"Pytest" matching MCP servers:

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    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.
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    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
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    MIT
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    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.
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    Apache 2.0
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    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
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    MIT
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    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.
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    1
    MIT
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    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.
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    51
    MIT
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    Enables AI agents to investigate and repair Python/pytest repositories in isolated Git worktrees with audit trails, without modifying the original repository.
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    MIT
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    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.
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    MIT
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    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.
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    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.
    Last updated