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"Context compression techniques and methods" matching MCP servers:

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    An MCP server serving a curated corpus of thinking techniques for AI agents, with tools to classify intents, find and apply techniques, and verify their effectiveness.
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    A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.
    10
    MIT
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    Give your AI agents access to 8,500+ community curated awesome lists with over 1 million curated resources.
    2
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    57
    MIT
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    A high-performance MCP server providing up-to-date documentation for Go, npm, Python, Rust, Docker, Kubernetes, Terraform, and more — fetched from official sources, not training data.
    18
    3
    MIT
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    MCP proxy that reduces context usage through semantic tool routing, enabling on-demand discovery and routing of relevant tools.
    MIT
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    An MCP server that enables semantic search over local files or GitHub repositories by indexing content into a serverless vector database, allowing AI assistants to understand meaning rather than just keywords.
    24
    MIT
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    Local-first memory server that stores notes, contacts, and future data as a unified entity graph, providing hybrid retrieval (vector + keyword) for AI assistants via MCP.
    MIT
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    An MCP server that indexes a repository locally and provides keyword, semantic, hybrid, and SQL search tools, enabling coding agents to answer questions about the codebase efficiently without reading files one by one.
    51
    18
    Apache 2.0
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    Provides AI agents with a persistent, searchable knowledge library via MCP tools, allowing them to create books, manage pages, perform semantic search, and retrieve usage guides.
    5
    MIT
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    Local MCP server that provides semantic search (RAG) over code repositories, enabling AI clients like Claude and Gemini to access project context without manual re-upload.
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    Enables semantic search across documentation stored in Gemini FileSearchStores, returning AI-generated answers with source citations.
    1
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    Enables schema-aware exploration of JSON data by uploading samples, flattening nested structures, and using heuristic search with token overlap and fuzzy matching to find field paths for target names, accelerating ETL and API onboarding workflows.
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    A small MCP server that gives agents rich context about a YouTube video — its transcript, jump-to-the-moment deep links, metadata, and most-replayed moments — so they can answer questions, summarize, pull quotes, or surface highlights.
    7
    2
    MIT
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    Exposes tools for AI assistants to query a persistent SQLite+FTS5 index of C/C++ symbols parsed from real build commands, enabling sub-millisecond lookup, full-text search, and natural-language explanation without hallucination.
    34
    7
    MIT
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    Enables AI-driven semantic code search using Windsurf's reverse-engineered SWE-grep protocol to query local codebases with natural language. It executes local search tools like ripgrep and tree-node-cli to return relevant file paths and line ranges to MCP-compatible clients.
    2
    229
    MIT
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    An MCP server and CLI tool that transforms codebases into AI-ready context through semantic search, call graph analysis, and incremental indexing. It enables AI assistants to perform hybrid vector and keyword searches to understand complex repository structures and cross-file relationships.
    5
    12
    1
    MIT
  • A
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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.
    5
    5
    MIT