"Campaign Monitor" matching MCP servers:

Enables AI assistants to monitor GitHub repository health by calculating health scores, fetching repository metadata, analyzing Dependabot alerts, checking CI/CD status, and retrieving code scanning alerts.

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 deployment and management of applications on the Citizen platform from git repositories or local files. It allows AI assistants to monitor deployment logs, handle authentication, and automatically fix build errors using intelligent error analysis.

Renders an interactive pipeline DAG and test-harness dashboard inline in MCP hosts, showing node statuses, pass rates, costs, and latency from a JSON state file.

Enables management of Railway.app infrastructure through natural language, allowing users to deploy services, manage environment variables, monitor deployments, and configure projects directly from MCP clients like Claude.

An MCP server that connects to the Google Play Developer API to deploy apps, manage releases, respond to reviews, and monitor app health through an AI assistant.

Enables AI agents to manage Databricks Lakeflow jobs by building and uploading Python wheels and triggering runs with specific arguments. It provides a structured way to orchestrate complex data experiments and monitor execution directly on Databricks clusters.

Monitor failing GitHub Actions workflows, auto-fix them with OpenCode, and retry the run — driven by a workflow_run webhook, coordinated through an MCP server.

Connects AI assistants to GitHub Actions workflows to monitor CI/CD pipelines, view run logs, diagnose failures, and optionally trigger or manage workflows with granular permission controls.

Enables AI systems like Claude and Cursor to directly manage Railway projects, deployments, services, environment variables, and monitor logs through natural language commands.

GitHub Actions Model Context Protocol Server that allows you to create, manage, monitor, and interact with workflows.

Enables AI agents to monitor and debug GitLab CI/CD pipelines by providing tools to list pipelines, retrieve job logs, and analyze failure root causes. It features automated error pattern extraction and failure analysis to streamline pipeline troubleshooting.

An MCP server that wraps the Apache Airflow REST API, enabling clients to manage DAGs, monitor task instances, and handle workflows through a standardized interface. It provides comprehensive access to Airflow features including DAG runs, variables, connections, and XComs.

Enables interaction with Jenkins to manage jobs, trigger builds with parameters, and monitor build queues. It provides comprehensive access to build histories, console logs, and organizational folder structures via the Jenkins REST API.

Integrates with the Laravel Forge API to provide comprehensive management of servers and sites. It enables users to perform health checks, monitor logs, create resources, and execute deployment tasks through MCP-compliant tools.

Provides integration with Apache Airflow's REST API, allowing AI assistants to programmatically interact with Airflow workflows, monitor DAG runs, and manage tasks.

Enables LLMs to discover and launch Ansible job templates on Ansible Automation Platform, monitor job status, and retrieve outputs.

Monitors Jenkins CI builds in real-time, fetching build status and logs on failure to provide context for AI-assisted code fixes, enhancing developer workflow.

Enables AI applications to retrieve GitHub repository information, manage issues, and monitor workflow statuses through the GitHub REST API.

Industry-standard MCP server for AWX/AAP/Ansible Tower automation, enabling AI agents to manage job templates, launch and monitor jobs, and handle inventories and projects through natural language.