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    Enables exploring Steam store reviews and community discussion threads with server-side filtering, temporal analysis, and metadata context for understanding player sentiment and feedback beyond store page noise.
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    21 npm
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    BSD 3-Clause
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    tooltrim reduces the tokens agents spend re-reading bloated tool results. Run it as an MCP server exposing compress and expand_tool_output, or as a gateway in front of any upstream MCP server: it re-exposes the upstream tools unchanged and shrinks each result (HTML/JSON/logs/tables) before it reaches the model, keeping the relevant content only.
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    MIT
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    MCP server for koreafilings.com — AI-summarized Korean DART (전자공시) corporate disclosures, paid per call in USDC via the x402 protocol on Base. Tools: get_pricing (free), get_disclosure_summary (0.005 USDC).
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    MIT
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    Lets any AI agent score and simplify its own text before it reaches a human, using Flesch readability metrics and plain-language rewrites entirely on the local machine.
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    MIT
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    A FastMCP server that enables AI assistants to extract structured information from unstructured text using Google's langextract library through a secure, optimized Model Context Protocol interface.
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    MIT
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    Provides MCP-compatible AI clients with offline text analysis and rewriting tools, including statistics, extractive summaries, keywords, readability scores, case conversion, entity extraction, and diffing, all running locally without API keys or network calls.
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    MIT
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    Unofficial MCP server for working with Kagi without API access (you'll need to be a customer, tho). Searches and summarizes. Uses Kagi session token for easy authentication.
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    MIT
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    MCP Long Context Reader is a Python-based toolkit designed to overcome the context window limitations and high costs associated with Large Language Models (LLMs) processing extensive documents. It provides a FastMCP server with multiple, powerful strategies for an LLM agent to 'read' and query long documents without needing to load the entire text into its context window.
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    MIT
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    Delegates replaceable grunt work (boilerplate, formatting, translation, long-document summarizing) from a premium agent to cheap models behind a local LiteLLM proxy, auto-routing each task by type. Delegated calls run in a separate process, so the subscription session and the API credentials never share an environment.
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    20 PyPI
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    MIT
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    Provides 14+ character-level text analysis tools that give LLMs the ability to accurately count letters, analyze individual characters, and work with text at the character level—overcoming tokenization limitations.
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    An MCP server for intelligent YouTube video analysis that provides token-optimized summaries, sentiment analysis, and entity extraction from transcripts. It enables AI assistants to perform video reporting, channel monitoring, and comprehensive YouTube searches through structured data tools.
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    24 PyPI
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    Apache 2.0
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    A personal MCP for WhatsApp, with voice-note and call transcripts. Search and summarize your chats, read what was said in voice notes and calls, and organize chats, labels, notes, reminders and scheduled messages. Runs locally with the Kaption extension for WhatsApp Web (Chrome, Edge) or the Kaption desktop app. No send-now tool. Setup: https://kaptionai.com/mcp/
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    178 npm
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    Business Source 1.1
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    Precise character-level string indexing for LLMs. Provides tools for finding, extracting, and manipulating text by exact character position to solve position-based operations.
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    MIT
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    A Node.js implementation of the Kagi Model Context Protocol server that enables Claude AI to search the web and summarize documents, videos, and audio using Kagi's APIs.
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    10 npm
    MIT
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    An MCP server for managing and persisting notes, offering CRUD operations, note summarization, and resource-based access via a note:// URI scheme.
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    MIT
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    Fetches and processes Hacker News discussions to prepare them for Claude to generate high-quality summaries, handling comment structure and metadata to help Claude understand the relative importance of different comments.
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    MIT