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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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    MIT
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    Enables querying Margint per-customer AI margins and costs from MCP clients. Provides tools to list customers, drill into costs, check budgets, and view workspace-wide cost breakdowns.
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    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.
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    MIT
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    ac-infinity-mcp is an MCP server that connects Claude (and other AI assistants) directly to your AC Infinity controllers — so you can read live sensor data, run analytics, and adjust fan speeds or port states using natural language, without opening the AC Infinity app.
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    MIT
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    Software end-of-life intelligence for AI agents: EOL dates, support timelines and 0-100 upgrade risk scores for 480+ products. Check whether a version is still supported, score its risk, or audit an entire stack.
    5
    MIT
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    Enables AI agents to query Windows process, window, and console information via structured JSON instead of screenshots, reducing token usage by 94-98%.
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    MCP plugin that alerts you when AI token usage is wasteful. Fires warnings, errors, and alerts on large outputs, verbose logs, and repetitive history, auto-suppressing noise to keep your context lean.
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    Enables LLMs to query telemetry data via the Spyglass AI agent, providing intelligent insights about application performance, errors, and bottlenecks.
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    MIT
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    Enables AI agents to manage a Canonical Landscape estate, including inventory, alerts, patching, and script execution, with built-in safety layers to prevent accidental destructive actions.
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    Provides a standardized MCP interface for querying Graylog logs, enabling AI agents to search, diagnose, and correlate runtime logs with code via configurable profiles.
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    MIT
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    Estimates the environmental footprint of your AI use — energy (kWh), miles driven, water used for cooling, and CO₂ — plus a prompt-efficiency score, working with any AI client by measuring token usage.
    9