diff --git a/README.md b/README.md index f701fa89..b9a1bd83 100644 --- a/README.md +++ b/README.md @@ -1196,6 +1196,7 @@ Access and analyze application monitoring data. Enables AI models to review erro - [mpeirone/zabbix-mcp-server](https://github.com/mpeirone/zabbix-mcp-server) ๐Ÿ โ˜๏ธ ๐Ÿง ๐ŸชŸ ๐ŸŽ - Zabbix integration for hosts, items, triggers, templates, problems, data and more. - [netdata/netdata#Netdata](https://github.com/netdata/netdata/blob/master/src/web/mcp/README.md) ๐ŸŽ–๏ธ ๐Ÿ  โ˜๏ธ ๐Ÿ“Ÿ ๐ŸŽ ๐ŸชŸ ๐Ÿง - Discovery, exploration, reporting and root cause analysis using all observability data, including metrics, logs, systems, containers, processes, and network connections - [pydantic/logfire-mcp](https://github.com/pydantic/logfire-mcp) ๐ŸŽ–๏ธ ๐Ÿ โ˜๏ธ - Provides access to OpenTelemetry traces and metrics through Logfire +- [Higangssh/homebutler](https://github.com/Higangssh/homebutler) ๐ŸŽ๏ธ ๐Ÿ  - All-in-one homelab management MCP server. Monitor system resources, manage Docker containers, Wake-on-LAN, scan networks, check open ports, and run alerts โ€” across multiple servers via SSH. Single 10MB binary, zero dependencies. - [seekrays/mcp-monitor](https://github.com/seekrays/mcp-monitor) ๐ŸŽ๏ธ ๐Ÿ  - A system monitoring tool that exposes system metrics via the Model Context Protocol (MCP). This tool allows LLMs to retrieve real-time system information through an MCP-compatible interface.๏ผˆsupport CPUใ€Memoryใ€Diskใ€Networkใ€Hostใ€Process๏ผ‰ - [speedofme-dev/speedofme-mcp](https://www.npmjs.com/package/@speedofme/mcp) ๐Ÿ“‡ โ˜๏ธ ๐ŸŽ ๐ŸชŸ ๐Ÿง - Official SpeedOf.Me server for accurate internet speed tests via 129 global Fastly edge servers with analytics dashboard and local history - [TANTIOPE/datadog-mcp-server](https://github.com/TANTIOPE/datadog-mcp-server) ๐Ÿ“‡ โ˜๏ธ ๐ŸŽ ๐ŸชŸ ๐Ÿง - MCP server providing comprehensive Datadog observability access for AI assistants. Features grep-like log search, APM trace filtering with duration/status/error queries, smart sampling modes for token efficiency, and cross-correlation between logs, traces, and metrics.