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"A resource for understanding Claude's context window and how it works" matching MCP servers:

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    An MCP server that exposes a perpetual, honest job-application pipeline as typed tools an LLM agent can call, with fit scoring, verified resume building, and a submission planner enforced by code, not prompts.
    16
    MIT
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    A comprehensive Model Context Protocol server providing access to 70+ IT tools for developers and system administrators, including encoding/decoding, text manipulation, hashing, and network utilities.
    34
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    TypeScript
    MIT
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    Local MCP server for A-share stock trading via Tonghuashun, offering account/position queries, buy/sell/cancel orders with risk controls and forced user confirmation; currently simulated with a reserved interface for real broker channels.
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    Enables interaction with the FVWM3 window manager for configuration generation, window management, and debugging. Provides access to config files, runtime state, and control tools for managing desktops, monitors, and tiling operations.
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    An AI-centric MCP server that enables automated Xilinx Vivado workflows, including project management, synthesis, implementation, and timing analysis. It allows AI agents to drive hardware design processes while integrating directly with the official Vivado GUI for visual context.
    15
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    Provides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.
    32
    Apache 2.0
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    Dynamic QR code platform for AI agents. Create, customize, and track QR codes without regenerating images. 37 tools covering 11 QR types (URL, vCard, WiFi, event…).
    37
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    MIT
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    Enables access to Lose It calorie tracking data including daily summaries with budgets and remaining calories, weekly history, and food logs. Integrates with the Lose It web app API to retrieve nutrition information and dietary entries for monitoring caloric intake.
    3
    MIT
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    Helps users decide what to eat by randomly selecting meals from a database of 100+ options or a custom list using an interactive spinning wheel widget. It supports cuisine filtering and provides an engaging visual interface to resolve mealtime indecision.
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    Enables AI models to interactively prompt users for input or clarification directly through their code editor. It facilitates real-time communication between assistants and users during development tasks.
    MIT