Browse all content tagged with Observability
The Logfire MCP Server connects AI assistants and LLMs to telemetry data via OpenTelemetry, enabling real-time querying, exception monitoring, root cause analysis, and custom SQL over your distributed traces and metrics using FlowHunt.
The Cloudflare MCP Server bridges AI assistants and Cloudflare’s cloud services, enabling natural language automation for configurations, logs, builds, and documentation. Easily integrate Cloudflare’s APIs and observability into AI-powered workflows with secure, robust setup.
The Cronlytic MCP Server brings seamless AI-powered automation to serverless cron job infrastructure, enabling LLMs to manage, monitor, and optimize scheduled tasks through a unified Model Context Protocol (MCP) interface.
Integrate and automate Grafana’s dashboards, datasources, and monitoring tools into AI-driven development workflows using FlowHunt's Grafana MCP Server. Enable seamless AI-powered dashboard management, query execution, and observability integration.
The Dynatrace MCP Server connects the Dynatrace observability platform to your AI-powered workflows in FlowHunt, enabling real-time access to production metrics, logs, anomalies, and security events for enhanced debugging, monitoring, and automation.
The Honeycomb MCP Server bridges AI assistants and Honeycomb observability data, enabling LLMs to securely analyze metrics, dashboards, and code behavior for enterprise workflows. It automates data analysis, accelerates root cause investigation, and streamlines SLO management within developer operations.
The Datadog MCP Server bridges FlowHunt and Datadog’s API, enabling AI-driven access to monitoring data, dashboards, metrics, events, and logs for advanced observability and automated incident management.
The Netdata MCP Server connects AI assistants and automation tools with the Netdata monitoring platform, allowing real-time access to system metrics and streamlined observability workflows via a protocol-driven interface.
The Loki MCP Server connects AI assistants with Grafana Loki, allowing seamless querying and analysis of log data via the Model Context Protocol. It empowers LLM-driven workflows for log exploration, troubleshooting, and dashboard creation.
The Simple Loki MCP Server integrates Grafana Loki log querying into AI workflows via the Model Context Protocol. It enables AI agents to analyze, filter, and retrieve logs for observability, troubleshooting, and monitoring, using LogQL queries with flexible authentication and multiple output formats.
The Tempo MCP Server enables AI assistants to query and analyze distributed tracing data from Grafana Tempo, streamlining observability, debugging, and system performance analysis for distributed applications.
