Enterprise AI operator evaluation MCP server. 27 tools
(22 read + 5 write) for measuring, benchmarking, diagnosing, and interv
ening on how human operators use AI tools across 5 canonical metrics.
Radar is an open-source Kubernetes observability and diagnostics MCP server. It exposes cluster health, workload diagnosis, logs, events, topology, audit findings, and remediation actions to AI agents through the Model Context Protocol.
Enables AI assistants to search, analyze, and debug application API traffic captured by Tusk Drift, including HTTP requests, database queries, distributed traces, latency metrics, and error rates.
Fetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.
MCP server for Drumbeats monitoring. Enables creating monitors, triaging incidents, and running HTTP/SSL/DNS checks using natural language from any AI client.
An MCP server exposing 72 tools across 26 homelab services, enabling LLMs to monitor and manage infrastructure, media, storage, and networking with a single endpoint.
Governed, read-only OT data tap for substation and utility telecontrol — IEC 60870-5-104, DNP3/IEEE 1815, and IEC 61850 MMS connectors with cross-protocol asset discovery, alarm and downtime RCA, unbypassable audit logging (MCP + CLI), budget/runaway guards, and an airgap no-egress mode. Energy edition of Industrial-AIOps, built on iaiops.core.
Zero-config MCP server that gives AI coding assistants a real-time diagnostic snapshot of your local dev environment. Detects framework, running services, recent errors, git state, and provides a health diagnosis in one call.
Estimates the environmental footprint of your AI use — energy (kWh), miles driven, water used for cooling, and CO₂ — plus a prompt-efficiency score, working with any AI client by measuring token usage.
A Model Context Protocol server that enables AI assistants to interact with Sentry for error tracking and monitoring, allowing retrieval and analysis of error data, project management, and performance monitoring through the Sentry API.
Enables MCP clients to interact with a local AI agent through Ollama, providing tools for checking weather, current time, and available models, while tracing calls to Langfuse.