"A hub for monitoring MCP trends" matching MCP servers:

Connects AI coding assistants to Gaffer test history and coverage data to analyze project health, debug failures, and identify untested code areas. It enables tools to track test stability, cluster failures by root cause, and monitor code coverage trends across projects.

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.

An MCP server that enables AI assistants to interact with Apache Airflow's REST API for DAG management, task monitoring, and system diagnostics. It provides comprehensive tools for triggering workflows, retrieving logs, and inspecting system health across Airflow 2.x and 3.x versions.

MCP server for JetBrains Space merge requests and Patronus CI dry runs, enabling creation, viewing, and management of merge requests as well as starting and monitoring CI dry runs.

Enables inspection and management of Apache Airflow DAGs, runs, and logs across multiple instances. It provides tools for monitoring workflows and performing gated write operations like triggering DAGs or clearing task instances.

An intelligent MCP server that gives AI agents full control over GitHub Actions CI/CD pipelines, including real-time monitoring, log analysis, AI-powered failure diagnosis, and deployment management.

AI Gateway - MCP server providing AI-powered tools and automation by MEOK AI Labs

Enables management of Railway.app infrastructure through natural language, including deploying services from GitHub repos or Docker images, managing environment variables, monitoring deployments, and configuring networking and volumes.

Enables AI assistants to manage CData Sync operations, including data synchronization jobs, connections, and ETL processes through stdio or HTTP transports. It provides tools for executing jobs, monitoring real-time progress via Server-Sent Events, and handling comprehensive workspace configurations.

Enables management of Railway.app infrastructure through natural language, including deploying services, managing environment variables, monitoring deployments, and handling databases and volumes.

Enables management of Railway.app infrastructure through natural language, including deploying services, managing environment variables, monitoring deployments, and controlling project resources.

Enables monitoring and debugging of Hatchet workflows and jobs through MCP clients like Claude Code. Users can list workflows, track run statuses, query queue metrics, and retrieve job results using natural language commands.

MCP server that connects AI agents to Jenkins, supporting stdio and HTTP transports with tools for jobs, builds, logs, and queue management.

Enables monitoring and analysis of Ansible playbook executions through the Ara Records API. Provides real-time playbook monitoring, task progress tracking, and comprehensive access to Ansible execution data.

Enables managing GitHub Actions workflows through MCP, including listing and inspecting workflows, triggering workflow_dispatch and repository_dispatch events, and monitoring or canceling workflow runs.

Universal deployment tracker for AI assistants, enabling real-time monitoring and management of deployments across Vercel, Netlify, and Cloudflare Pages without context switching.

Enables test execution and management through MCP clients, allowing retrieval of projects, listing tests, executing tests with browser selection, and monitoring results.

Enables management of Amazon Managed Workflows for Apache Airflow (MWAA) environments and operations including DAG management, workflow execution monitoring, and access to Airflow connections and variables through a unified interface.

A specialized MCP server for QA engineering. It provides tools for user story analysis, test strategy, BDD/Gherkin scenarios, contract testing, integration tests, performance plans, security checklists, and CI/CD pipeline generation, accessible from any MCP-compatible IDE or client.

Enables AI assistants to interact with Coolify self-hosted instances for application deployment, management, and monitoring. Features 4 unified tools optimized for VS Code's limits, covering app management, environment configuration, system administration, and built-in documentation.