"A service or information about interpreters" matching MCP servers:

Exposes the shared health-check service as an MCP tool, letting clients query status, uptime, and version through the streamable HTTP endpoint.

Provides tools to communicate with a service dependency API, enabling users to query and interact with service relationship data and dependency mappings.

Enables business services to push error logs and generate diagnostic reports using MCP tools for log searching, code querying, and report generation.

A professional-grade MCP server that enables AI assistants to monitor web services, APIs, and HTTP endpoints with enterprise-level security.

Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.

An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.

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

Enables AI assistants to search, summarize, and inspect Kibana/OpenSearch Dashboards logs through the user's logged-in browser session, eliminating the need for API keys or service accounts.

An MCP server for Grafana Loki that enables LLMs to query, tail, and analyze logs, featuring fuzzy container-name matching, compact output, and tools for log patterns, volume, and label discovery. It can run as a local stdio server or a containerized HTTP service.

Connects AI assistants to Crontinel, enabling natural language queries about cron jobs, queues, Horizon status, and alerts without opening a browser.

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The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.

A Model Context Protocol server that provides AI agents with controlled read access to Datalust Seq instances for log analysis and monitoring. It enables agents to search events, execute data queries, and retrieve information about signals, dashboards, and alerts.

Enables LLM clients to ask natural-language questions about nodes in a FABRIC slice, returning grounded answers from the Node Exporter Full dashboard's fixed PromQL queries.

A Model Context Protocol (MCP) server for the Pingera monitoring service, providing seamless integration between AI models and monitoring data.

Enables a local AI agent to investigate service incidents by querying read-only telemetry such as service health, metrics, logs, and traces, then produce verifiable assessments with evidence links and uncertainty.

Enables read-only observability of Windows systems, exposing CPU, memory, storage, service, and health information through Model Context Protocol tools.

Read-only MCP server for TrueNAS Scale debugging, providing k3s tools for kubectl operations and whitelisted midclt calls for system info, apps, and pools.

Enables AI agents to interact with the Honeybadger error monitoring service to list, filter, and analyze fault data. It provides tools for fetching error lists and retrieving detailed fault information from Honeybadger projects.

A local TypeScript MCP gateway that unifies SSH, Prometheus, and MySQL tools under a single loopback HTTP service, featuring a web console for configuration and health monitoring.