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    A Model Context Protocol server that provides AI assistants access to AWS CloudWatch Logs, enabling browsing, searching, summarizing, and correlating logs across multiple AWS services.
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    Apache 2.0
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    Enables natural language queries of the Chrome UX Report API to fetch Core Web Vitals (LCP, INP, CLS, FCP, TTFB), compare form factors, benchmark pages against site averages, and analyze up to 40 weeks of performance trends.
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    134
    MIT
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    MCP server for Uptrack uptime monitoring. Manage monitors and incidents from AI agents like Claude, ChatGPT, and Cursor.
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    MIT
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    A lean MCP server that provides LLM agents with transparent access to multiple Prometheus instances for metrics analysis and SRE operations.
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    GPL 2.0
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    Enables AI assistants to query, search, and analyze logs across Google Cloud Platform projects. It supports advanced filtering by severity or resource type and provides detailed log entry retrieval and project listing capabilities.
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    MIT
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    Enables natural language analysis of network packet captures, including protocol detection, flow analysis, and security threat identification, integrated with AI assistants.
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    MIT
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    Exposes Modal cloud functions as MCP tools for video rendering, voice synthesis, and app management. Enables LLM agents to execute Remotion renders, F5-TTS voice cloning, and monitor Modal applications via Supabase-backed infrastructure.
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    Enables AI agents to integrate with IBM Storage Insights for seamless observability and diagnosis of registered storage assets. It provides tools to retrieve alerts, performance metrics, and system configurations via the IBM Storage Insights External APIs.
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    Apache 2.0
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    Records runtime events from any app via zero-code instrumentation and exposes them to LLMs through MCP for autonomous debugging.
    11
    MIT
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    Windows tray process that exposes local ambient context (presence, foreground app, battery, etc.) as MCP tools with privacy classification and opt-in controls.
    3
    MIT
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    A modular Retrieval-Augmented Generation (RAG) framework that provides hybrid search and knowledge retrieval capabilities via the Model Context Protocol. It enables users to integrate document-based knowledge into LLM workflows with support for dense/sparse retrieval, reranking, and observability.
    1
    MIT