"GitHub MCP server" matching MCP servers:

Enables GitHub Copilot Agent Mode to act as a QA assistant by providing MCP tools to query and modify Bugzilla bugs, Jira issues, and Confluence pages through natural language.

Enables coding agents to verify whether a GitHub pull request is genuinely ready by checking draft state, conflicts, check results on the head commit, review requirements, merge queue status, and expected commit, returning a verdict with reasons and owners pinned to a commit and timestamp. It reports merged or closed PRs without judging them and never authorizes a merge.

Enables AI coding tools to run CheckMyLaunch scans on GitHub repos or live URLs and retrieve findings and repair prompts directly within the chat.

Local-first bug reports for coding agents: annotated screenshots, recordings and notes stored on your machine, served over MCP with the images included. Sync reports to GitHub issues when you choose.

Translates a lockfile diff into a human-readable upgrade plan. For every dependency bump (npm or PyPI) returns semver class, breaking changes from GitHub release notes, CVEs fixed in the range, migration guide links, and a clear per-package recommendation. Bulk tool ranks up to 50 package changes in parallel by risk (security > caution > review > likely-safe > safe).

Enables AI assistants to summarize Playwright test runs, identify flaky tests, heuristically triage failures, generate Playwright test stubs, and retrieve recent GitHub CI status through MCP tool calls.

AI 規格大師 — MCP server bridging specs (Linear / JIRA / GitHub Issues / Notion / Markdown / Figma) to tests, with bidirectional traceability and a spec-quality coach. Sibling to mk-qa-master.

Automates the evaluation of GitHub hackathon submissions by providing tools to collect issue data, apply scoring rubrics, and generate ranking reports. It enables a multi-agent system to manage scoring consistency, statistical outlier detection, and feedback generation through the GitHub CLI.

MCP server that lets Claude diagnose failed GitHub Actions runs by fetching and parsing CI logs into structured failure signals, enabling natural-language questions like 'Why did the last run fail?'.

Scores npm packages, PyPI packages and GitHub repositories 0-100 with an A-F grade and plain-language risks. Scans package.json, requirements.txt and lock files; no API key needed.

Lets AI coding agents list captured webhooks, explain why a signature failed, replay events to localhost and generate handler code. Works with Stripe, GitHub, Shopify, Slack and Twilio.

AI-powered website feedback and visual bug reporting MCP server that lets AI coding assistants access Vynix projects, annotations, and create GitHub issues with rich debugging context.

Runs a project's Playwright or CTRF test suite and returns a redacted failure packet (failing test, file and line, console errors, failed requests, rerun command) so a coding agent can fix and rerun. Also reports whether a commit has a signed, passing test receipt on GitHub.

Lightweight Node.js dependency vulnerability audit tool with CLI and MCP Server modes. Supports npm/pnpm, full dependency chain tracing, remote GitHub repo auditing, and generates Markdown/HTML reports.

Browser acceptance testing for AI agent deliverables: verify agent output in real Chromium from JSON specs, with MCP server, visual regression, multi-browser support and GitHub Actions.

This MCP server lets GitHub Copilot fetch Azure Boards work items by URL or ID and generate QA test cases from the ticket content.

Turn screen recordings into automated Playwright E2E reproduction tests, structured GitHub bug reports with clickable video timestamps, and SOP walkthroughs.

AI-powered code review tool that detects AI-generated code defects invisible to traditional linters — hallucinated packages, deprecated APIs, cross-file contradictions, hidden security anti-patterns, and over-engineering. Works as a standalone CLI, GitHub Action, or MCP server. Supports TypeScript, Python, Java, Go, and Kotlin. Free for individuals, no API key required.

Provides tools to analyze and debug GitHub Actions CI failures, including summarizing failures, detecting flaky tests, and suggesting fixes.

Enables AI coding assistants to inspect GitHub Actions CI runs, diagnose failures with failed-step summaries and filtered job logs, read per-job coverage, and view pull request checks through read-only, allowlisted access.

An MCP server that connects to your GitHub repositories, detects API changes, and provides context for Cursor to automatically generate test cases.

MCP server for end-to-end QA automation: generates test scenarios, discovers Playwright locators, creates TypeScript test code, executes tests, and creates GitHub issues for failures.

Enables extracting only the minimal AST closure for a target feature from GitHub, local, package-registry, or bundled JavaScript references, replacing third-party dependencies with zero-dependency shims and adapting the code to a host project's language and style. It also generates runnable parity tests that verify 1:1 behavioral equivalence via Golden I/O vectors.

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.

Enables AI agents to capture screenshots from Android devices and emulators, and manage connected devices for UI analysis and testing. Supports device listing, screenshot capture, and integrates with Claude Desktop, Gemini CLI, and GitHub Copilot.

Automates the conversion of Figma designs into TypeScript React components and integrates with GitHub to create pull requests for the generated code. It includes visual regression testing with Playwright and accessibility validation to ensure implementations match the original designs.

Allows AI assistants such as Cursor/Cline/GitHub Copilot to use Google's lighthouse tool to measure perf metrics for your webpage. You can then run an agentic loop and get the assistants to optimize those metrics!

Enables comprehensive GitHub PR reviews through Cursor's AI by fetching PR diffs, running static analysis tools (ESLint, Prettier, TypeScript, Semgrep), executing tests, and generating detailed code review reports with inline comments.

Provides six MCP tools for AI-reliability work: grading LLM stacks from call logs, verifying source compilation, watching GitHub release drift, dry-running browser plans, auditing Actions workflows for fail-open settings, and gating drafts on human approval.

Enables AI agents to scan GitHub repositories for security vulnerabilities, deployment blockers, and code quality issues. It provides detailed findings and auto-generated code patches to help developers ensure their code is production-ready.

Converts non-functional requirements and code into secure, traceable JMeter performance tests with coverage analysis, plan generation, execution, and GitHub draft PRs.

Enables MCP clients and LLM agents to fetch pull-request diffs, map changed files to tests, run affected tests in a sandboxed workspace, and post review comments or labels to GitHub, with dry-run and fixture demo modes.

Lets an AI coding agent journal its edits, commands, checkpoints, intents and test evidence locally, then compile them into a machine-readable Change Package plus a human-readable review summary with statically computed risks. Optionally publishes the result as a GitHub Draft PR with inline comments on the exact diff lines, always gated behind explicit confirmation and redaction.

An MCP server for automated code review of GitHub pull requests. It checks security, quality, and license issues, and provides tools to list, inspect, and review PRs.

An MCP server that integrates with GitHub Copilot to analyze Axivion static-analysis reports and provide MISRA C:2012 compliance assistance. It offers deep rule explanations, rationale, and confidence-scored fix suggestions for identified code violations.

An AI-powered QA automation server that integrates Jira, GitHub, Jenkins, AWS, and Webex into a unified interface for QA engineers.

Enables an autonomous coding agent to fetch GitHub issues, safely inspect and patch code in isolated Docker sandboxes through standardized MCP tools, self-heal via iterative unit-test feedback, and open pull requests.

Connects AI assistants such as Claude, Cursor, and Windsurf to GitHub repository intelligence, code review, AST-based code and security analysis, web and documentation search, and automated test and documentation generation through MCP tools.

Analyzes API requirements and source code, evaluates existing Postman test coverage, and generates or updates test automation with an optional draft pull request on GitHub.

Connects Claude to GitHub Pull Requests to fetch and filter code diffs for AI-assisted reviews. It enables listing open PRs and analyzing changes while automatically excluding binary and asset files to focus on relevant code.