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"A guide for reducing token count in AI requests" matching MCP servers:

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    Provides tools for AI models to count characters and words in text, supporting English and other space-delimited languages.
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    MIT
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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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    6
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    MIT
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    Local-first dashboard + MCP server that parses Claude Code and Codex JSONL files into a SQLite cost / token tracker. Per-MCP and per-tool breakdown, session drill-down, dedup by request_id; never talks to vendor APIs
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    Enables AI agents to access and manage project guidelines, documentation, and context through a structured content system with template support and workflow management.
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    MIT
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    A beginner-friendly Model Context Protocol (MCP) server that helps users understand MCP concepts, provides interactive examples, and lists available MCP servers. This server is designed to be a helpful companion for developers working with MCP. Also comes with a huge list of servers you can install.
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    3
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    Apache 2.0
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    An MCP server that provides structural codebase indexing and surgical query tools to drastically reduce token usage through symbol-level searches and transitive impact analysis. It supports multiple languages and integrates with git to help AI agents understand code dependencies and the impact of changes in sub-millisecond time.
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    69
    1,094
    MIT
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    Enables AI coding assistants to execute shell commands locally, intercepting deterministic commands like git status and npm test before they reach the LLM, saving tokens and reducing latency.
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    MIT
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    Real-time Claude.ai subscription awareness for AI coding assistants. Surfaces live utilization, forecasts limits, gates expensive operations, and measures real per-task cost.
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    MIT
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    Token-safe code search for AI agents: queries the language-server index (clangd / Roslyn / tsserver / pyright) instead of grep and returns a token-capped file:line list — ~20x fewer tokens. Symbol-level editing + a grep→index rewrite hook. Local-only, no IDE.
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    10
    MIT
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    Enables academic research through paper search across multiple databases (IACR, CryptoBib, Crossref, Google Scholar), PDF processing, and GitHub repository browsing. Features modular architecture with FastMCP-based proxy server routing to specialized academic tools.
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    7
    2
    MIT
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    Automatically reduces token usage in Claude Code sessions using algorithmic optimizations like code compression, smart file reading, output summarization, and prompt rewriting, with no extra API calls or cost.
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    6
    1
    MIT
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    Reduces context window usage by 60-90% through intelligent caching, compression, and smart tool replacements for Claude Code and Claude Desktop.
    Last updated
    1,350
    453
    MIT