"Finding people on GitHub" 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.

Read-only MCP server that exposes the agentic-os governance, SDLC, and Quality Engineering methodology to any MCP host. It never writes to your repository and never executes code — it serves the methodology, plans an install, and verifies it, handing any commands back to the host to run.

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.

Screen-reader navigation cost analyzer. Measures how hard it is for assistive-technology users to discover, reach, and operate interactive targets on the web.

MCP server that exposes Dira ProSuite quality verification to AI assistants, enabling data quality checks on geospatial datasets through natural language.

MCP server for managing test cases, cycles, and results in Zephyr Scale on self-hosted Jira. Enables AI agents to create, search, update, and delete test artifacts through natural language.

Enables AI assistants to generate realistic, synthetic test data on demand, including valid PESELs, NIPs, addresses, and more across 27 EU countries, through two tools: list_generators and generate.

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.

MCP server providing tools to verify task completion (tests pass and changes match claim) and confirm bug reproduction before fixing.

Enables LLM agents to programmatically inspect, debug, and test other MCP servers by wrapping the MCP Inspector CLI. Supports listing and calling tools, reading resources, and testing prompts on both local and remote MCP servers.

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.

Exposes Semgrep as deterministic, rule-matching analysis tools over MCP so a code-review agent can scan only the lines changed in a diff, scan whole files on demand, and check analyzer availability, version, ruleset, and limits. Results are returned as compact per-finding lines with source snippets suitable as comment anchors, along with counts of suppressed out-of-diff matches.

Provides AI coding assistants with on-demand access to component specs, test scenarios, accessibility requirements, and build guides from the Web UI Component Specification.

Audits Excel workbooks by tracing cell dependencies and detecting circular references, volatile functions, and other formula risks, enabling natural-language audit queries on local files.

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?'.

Enables AI agents and MCP clients to drive real Chrome, Edge, Brave, and Firefox browsers through native automation protocols. It exposes tools for navigation, page snapshots, form filling, clicks, screenshots, console/network inspection, and tab management.

Enables AI agents to start, control, and verify local open.mp/SA-MP game servers via MCP stdio, using a headless RakClient to send allowed slash commands and assert round-trip responses.

Scans any website and produces an Agent Readiness Report scored on the open ASO framework.

Browser control for Claude Code, on Firefox.

An intelligent debugging assistant that automates the debugging process by analyzing bugs, injecting HTTP-based debug logs into code across multiple environments (browser, Node.js, mobile, etc.), and iteratively fixing issues based on real-time feedback.

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.

MCP server that lets AI editors execute JavaScript, read console output, and simulate clicks/input on static web pages. Supports multiple browser pages and can be deployed remotely via HTTP transport.

Exposes llm-behave's behavioral regression testing as MCP tools, allowing offline semantic similarity checks on LLM outputs without any API calls.

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.

Finds and removes AI writing tells with a deterministic ruleset. Runs locally by default; an optional pass can call a model if you turn it on.