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"MCP servers for monitoring application power and memory usage on Windows and macOS" matching MCP servers:

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    Enables async-first working practices by providing tools to draft decision docs, convert meetings to async artifacts, score status updates, and triage sync vs async tasks. It also offers reference tools for the Open and Async book's principles and coaching prompts.
    11
    122
    45
    Inno Setup
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    MCP server for querying OpenAI usage and cost data, including spend summaries, daily breakdowns, month-over-month comparisons, and token usage by model.
    3
    MIT
  • A
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    Exposes a get_context_usage tool that reports raw token usage from session transcripts for Claude Code and OpenAI Codex CLI, enabling agents to check context size and branch behavior.
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    22
    MIT
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    Monitors your Cursor usage and provides a conserve flag to the agent, helping you manage request quota by batching questions, preferring defaults, and cutting unnecessary confirmations.
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    MIT
  • F
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    Reports your Claude subscription usage (5-hour and weekly limits) with a forecast and velocity recommendation, using Claude Code's existing OAuth session without requiring an API key.
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    An MCP server that retrieves GitHub Copilot usage metrics and seat assignment data across Enterprise, Organization, and Team levels. It allows users to monitor code completions, chat activity, and active user counts through integrated tools.
    5
    1
    MIT
  • A
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    A privacy-first MCP server for locally managing job, fellowship, and graduate-school applications. It offers tools for tracking application status, analyzing role fit, generating LaTeX CV/cover letters, interview prep, and discovering public jobs from ATS APIs.
    8
    MIT
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    MCP server that wraps Cursor Enterprise API to enable AI agents to answer questions about team spending, usage, and model adoption.
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    MIT
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    Exposes Windows system information and control tools including hardware stats, network details, and process monitoring. It enables AI applications to retrieve real-time data about CPU, memory, drives, and active system processes.
    12
    GPL 3.0
  • F
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    Enables LLMs to read and write local user data, generate fake users via sampling, and interact with structured prompts and resources.
    2