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    MCP server that gives AI agents access to your application's OpenTelemetry traces for querying, analysis, and debugging.
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    MIT
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    MCP server for Drumbeats monitoring. Enables creating monitors, triaging incidents, and running HTTP/SSL/DNS checks using natural language from any AI client.
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    Apache 2.0
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    MCP server that provides read access to Plausible Analytics data with natural-language date resolution, enabling users to query analytics like 'yesterday' or 'last week' without needing to know exact date formats.
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    MIT
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    MCP server for the beacon log analytics platform, exposing beacon's REST API as MCP tools and resources for AI agents to query and analyse AI-assistant usage data.
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    MIT
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    Enables real-time tracking of AI task starts and completions with integrated webhook notifications for monitoring activity. It allows users to log progress and send task-related event data to external services via configurable webhook URLs.
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    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.
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    Connects MCP clients to TunnelHub to monitor automations, inspect executions, and analyze logs or traces. It enables users to manage environments and troubleshoot integration failures through natural language commands.
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    This project provides a Model Context Protocol (MCP) server that allows language models and other MCP clients to interact with a SuzieQ network observability instance via its REST API.
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    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.
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    A Claude Code MCP server that connects to Laravel Clockwork to provide insights into application performance, database queries, errors, and request flow.
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    Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.
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    Apache 2.0
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    A comprehensive, AI-powered performance analysis and monitoring platform for OpenShift/Kubernetes clusters. This project provides Model Context Protocol (MCP) servers for analyzing etcd, network, and OVN-Kubernetes components with deep performance insights, automated root cause analysis, and actionable recommendations.
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    Apache 2.0
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    Connect AI agents like Claude, Cursor, and Gemini to historical Windows performance and privacy data gathered by the AppControl Windows app. This server enables natural language queries into CPU, GPU, and temperature history alongside system security events for deep local auditing.
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    31
    MIT