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    Monitor and manage Apache Airflow clusters through natural language queries via MCP tools: DAG inspection, task monitoring, health checks, and cluster analytics without API complexity. * Guide: https://call518.medium.com/mcp-airflow-api-a-model-context-protocol-mcp-server-for-apache-airflow-5dfdfb2
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    Exposes the complete Zabbix API functionality through the Model Context Protocol, mapping API methods to tools for managing hosts, triggers, and monitoring data. It enables seamless integration and control of Zabbix monitoring environments via natural language interfaces.
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    The Seq MCP Server enables interaction with Seq's API endpoints for logging and monitoring, providing tools for managing signals, events, and alerts with extensive filtering and configuration options.
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    MCP Server for Raygun's API V3 endpoints for interacting with your Crash Reporting and Real User Monitoring applications. This server provides comprehensive access to Raygun's API features through the Model Context Protocol.
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    Enables AI assistants to query Cisco ThousandEyes v7 API for network monitoring data including tests, agents, alerts, dashboards, and test results (network, page-load, web-transactions, path visualization) for faster troubleshooting through natural language.
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    Apache 2.0
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    Enables LLMs to directly query a Matomo analytics instance, execute reporting methods, fetch historical trends, report metadata, and dynamically generated chart images through typed MCP tools.
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    A unified local API gateway providing caching, rate limiting, and full Model Context Protocol compatibility for AI agent integration. It enables users to aggregate multiple API endpoints into a single gateway with built-in observability and customizable eviction strategies.
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    Model Context Protocol server for Verlon AI that exposes gates, logs, recommendations, and experiments as MCP tools, enabling coding agents to inspect and manage AI infrastructure natively.
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    An MCP server that exposes GPU-accelerated anomaly detection to AI assistants via the Model Context Protocol. Provides two MCP tools: waveguard_scan (send training + test data in one call, returns per-sample anomaly scores and top explanatory features) and waveguard_health (check API and GPU status). Works on time series, JSON, numbers, text, and images — fully stateless.
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    Read-only MCP server for Umami analytics. It talks to the Umami REST API directly over HTTP, supporting self-hosted and cloud setups.
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    Enables AI assistants to access Scout Monitoring performance and error data through Scout's API. Provides traces, errors, metrics, and insights for Rails, Django, FastAPI, Laravel and other applications to help identify and fix performance issues like N+1 queries, slow endpoints, and memory bloat.
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