"MCP servers from GitHub user smriti-AA" matching MCP servers:

An MCP server for external computer-use that launches, observes, and interacts with any X11 application via AT-SPI2 accessibility tree and xdotool input injection.

Enables end-to-end testing of voice-enabled web applications by simulating a synthetic user with a virtual microphone, virtual speakers, a real browser, and a viewable display, allowing audio injection, speech capture/transcription, and browser automation.

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 learning and integration testing of the Model Context Protocol by evolving from a minimal server toward production-grade tools, resources, prompts, and agent interoperability.

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.

MCP server for measuring web performance with Google PageSpeed Insights, providing median scores with uncertainty and real-user data. It offers tools for scores, diagnostics, and comparisons to identify regressions.

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.

Creates, validates, audits, and repairs PowerPoint .pptx files against the OOXML spec, allowing users to generate spec-compliant slides from templates.

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.

Run real Playwright E2E tests from your AI coding agent.

Checks a website or local dev server with axe-core (WCAG 2.0/2.1 A and AA) and returns a fix prompt with real examples from the pages, for the AI tool to fix the source. Automated checks find roughly a third of accessibility problems.

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?'.

Generates realistic, referentially-coherent test data (SQL INSERTs, JSON, or CSV) from your database schema, resolving foreign keys and respecting constraints. Paste CREATE TABLE DDL or a JSON schema and get ready-to-run seed data with valid relationships.

Scans live web pages and single-page apps across multiple viewports to detect WCAG 2.1 AA accessibility violations and returns concrete, browser-verified fixes grounded in WCAG success criteria and techniques.

Enables generating JSON Schema from sample data, generating TypeScript types, validating data against schemas, creating mock data from schemas, and comparing schemas for differences.

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.

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.

Bug bounty platform for vibe coders. Post bugs from AI-generated code, browse bounties, apply to fix, submit solutions, and run website diagnostics from any MCP client.

Enables LLM agents to run self-healing JMeter performance tests via natural language, from environment setup to capacity discovery and SLO verdict reporting.

An MCP server that provides tools for certificate verification, equivalence proving, and pre-registration sealing, enabling AI agents to re-derive verdicts from artifacts rather than trust assertions.

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.

AI-native browser testing, directly from your coding agent.

MCP server for VibeCheck bug reports. Gives AI assistants direct access to screen recordings, console logs, network requests, user actions, and web vitals — right inside your editor.

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.

MCP server that gives AI coding agents full control over Cypress test execution, allowing them to run, debug, and iterate on E2E tests directly from MCP-compatible agents.