"Campaign Monitor" matching MCP servers:

An MCP server that provides monitoring tools for Codex agents, allowing them to create and wait on conditions using commands, log files, file events, and custom probes, replacing polling loops with event-driven blocking.

Identifies resource-intensive processes on macOS across CPU, memory, and network usage, with tools for detailed process filtering and system overview.

FastMCP server with read-only tools to monitor Redis — queue depths, Celery queue status, connected clients, server/memory info, and per-database key counts

Enables Claude to monitor CPU, memory, disk, processes, network connections, Docker containers, and system logs for system diagnostics and troubleshooting.

Provides system monitoring tools for VPS including CPU, memory, disk, network ping, top processes, and Docker status, with read-only architecture and security features.

MCP server for monitoring health of MCP servers, GitHub Actions, GitLab pipelines, and HTTP endpoints with health history, TLS expiry, assertions, diagnostics, and alerts.

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

MCP-native health monitoring that probes MCP servers using the list_tools protocol handshake, detects version drift, stores history in SQLite, and generates an HTML dashboard.

SLA observation broker for the A2A network: agents register public health endpoints with target uptime and latency, and the shim probes them to record breaches.

Enables AI assistants to monitor and analyze Proxmox VE virtualization environments via the MCP protocol.

Plug-and-play homelab dashboard in one container — GPU, local-AI VRAM, Docker, systemd, host health. Built-in read-only MCP server so AI agents can explore it too.

Monitor and analyze AWS ECS services, including service status, CloudWatch metrics, and load balancer integration.

Real-time conversation health monitor for AI agents that tracks structural dynamics like semantic drift and information gain using deterministic arithmetic, without calling an LLM.

Provides read-only monitoring of Apache Airflow DAGs, including listing active DAGs, fetching recent runs, and analyzing execution history and performance metrics.

Multi-machine system monitor with a built-in MCP server that enables AI agents to query health metrics, manage processes, schedule cron jobs, and run diagnostics across local and remote machines.

MCP server that analyzes AI agent execution logs to calculate reliability scores, detect failure patterns, and suggest concrete improvements for making AI agents more reliable.

Monitor website health, uptime, SEO, security and performance via your AI assistant.

Paid remote MCP server for monitoring AI agent runs, detecting failures, replaying tool-call incidents, issuing SLA receipts, and exporting client status.

Enables AI clients to access real-time system information such as memory and swap usage from a Linux machine via MCP tools.

Provides real-time monitoring of AI agents, context, usage limits, workflows, files, Git, tests, builds, errors, secrets, and model-economy advice for tools like Claude Code, Codex, and Cursor, with 30 MCP tools for comprehensive observability.