"How to take a screenshot on a PC" matching MCP servers:

. A good description should be concise and describe what the server enables users to do, e.g. * Enables to interact with WordPress sites...' Then a huge block? Actually the user message includes the README content inside tags, then after the readme, there is a large block of text starting with '{

Enables intelligent access to AL-Go documentation, workflows, and domain expertise for Business Central development automation through specialized personas and cross-source semantic search.

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.

A hosted remote MCP server for managing Android release gates, with tools to check release status, explain permission blockers, issue release receipts, summarize device matrices, and export release logs.

Enables AI to directly read, edit, and compile PowerBuilder PBL projects.

Supply-chain gate for AI agent skills and MCP servers: poison-scan tool definitions, hash-pin the vetted set into a lock file and verify drift in CI. truecopy-mcp is a drop-in stdio proxy that filters a live server's tools/list down to its pinned, unmodified, unpoisoned tools.

AI code review against your team's own rules from your repository, before the pull request: two tools, review_changes and get_team_rules. Closed-source binary distributed via GitHub Releases; published in the official MCP Registry as dev.reviewgate/reviewgate.

A security-focused MCP server for self-hosted n8n Community Edition, providing 44 bounded tools to manage workflows, nodes, executions, credentials, tags, and more via the n8n Public API.

Generates a daily standup note from GitLab merge request or GitHub pull request state: what is ready to merge, what is blocked, what is stale, and what is waiting on your review. When CI is red it opens the failed job's log and reports the error lines, so the note says why the pipeline failed rather than just that it did.

MCP server with the watch_ci tool that polls GitHub Actions until a commit's CI run finishes, then returns the outcome and failed log tail in one call.

A lightweight, powerful, and flexible workflow engine that executes tasks defined in YAML configuration files.

A Model Context Protocol server for checking package versions and metadata across multiple package managers including npm, PyPI, Maven, NuGet, RubyGems, Crates.io, and Go modules.

Uptime monitoring your AI agent can set up: create HTTP, SSL, ping, TCP and DNS monitors checked from multiple regions, run checks on demand, read and acknowledge incidents, manage cron heartbeats, maintenance windows and status page updates.

tailtest-cline is a Cline plugin that automatically generates and runs tests every time the agent edits a source file. https://github.com/avansaber/tailtest-cline

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.

Provides MCP tools to get a single-call snapshot of git and GitHub Actions CI status across all repositories in a folder, flag missing or stale architecture docs, write doc files, and detect release drift between source and release repositories.

Enables scanning Python repos for dead code and automatically drafting a branch + PR for the safe subset (unused single-name imports) after running the repo's full test suite. All other findings are reported without modification, and a human always merges.

Automates the full setup and deployment of Ballerina + Apache ActiveMQ integrations inside a WSO2 Integrator workspace, from broker validation to a runnable BI-low-code-ready project.

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

Record an agent's MCP tool-call workflow once, replay it deterministically for zero tokens, and get a receipt — a snapshot-testing and deterministic-execution layer for Agent Skills.

A Model Context Protocol server for Bitbucket Cloud that lets agents read pull request diffs, comments, and pipeline logs, as well as write PR comments and edit PR details.

Stops your AI from re-introducing bugs, leaking provider keys, or weakening tests. Bug fixes become permanent regression guards; blocked mistakes become AI lessons the agent reads and learns from before its next edit.

Deploy, run and manage apps on your own self-hosted AppCrane from any MCP client. 62 tools: create an app, push code, deploy to sandbox, check health and logs, provision a managed Postgres, MariaDB, MongoDB or Redis, and promote to production after review. Your instance, your SSO, your data. Repo: https://github.com/gitayg/appcrane-mcp · npm: appcrane-mcp · Site: https://appcrane.dev

Enables AI coding agents to compute a 0-1 diff risk score for the current branch or any pull request, exposing the driving signals, evidence, tier and resulting auto-approve or human-review policy decision. Agents can use it to decide whether a low-blast-radius, AI-reviewed PR is safe to merge automatically while flagging risky changes for a human.

A Model Context Protocol server that monitors shadow-cljs builds and provides real-time build status updates for ClojureScript projects, allowing LLMs to verify build status after making code changes.

Enables code-review gating by converting a git diff into a typed verdict with safety probabilities, severity, and recommended actions for auto-merge or human review.

Deploys and manages projects on Swigs Cloud from any MCP client, turning folders or git repositories into live HTTPS sites in about two minutes.

A read-only Model Context Protocol server that converts local Git changes and JUnit XML reports into traceable release evidence, risk findings, and a rollback checklist.

A Model Context Protocol server that enables AI assistants to interact with Azure DevOps services, allowing users to query work items with plans to support creating/updating items, managing pipelines, handling pull requests, and administering sprints and branch policies.

Enables AI agents to deploy, manage, and configure web applications on sota.io, including project creation, deployment, environment variables, custom domains, and rollbacks.