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    An MCP server for indexing and searching local text files using late-interaction retrieval (ColBERT-style MaxSim), enabling token-level relevance matching.
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    MIT
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    Enables searching and retrieving documents from a local folder to ground LLM answers in your files.
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    MIT
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    Open registry of agent instruction files — system prompts, skills, workflows, and domain packs. Exposes the OpenClaw registry via 4 MCP tools: search by keyword/category, fetch full instruction files, list categories, and get top-rated files. CC0 licensed, free to use.
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    Jinni is a tool to efficiently provide Large Language Models the context of your projects. It gives a consolidated view of relevant project files complete with metadata, overcoming the limitations and inefficiencies of reading files one by one. The philosophy behind this tool is that LLM context wi
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    Apache 2.0
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    A unified knowledge retrieval tool for MCP-compatible LLMs, enabling structured external knowledge access from local files and future integrations.
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    An MCP server that enables users to download webpages as markdown files using r.jina.ai service, with features for configurable download directories and automatic date-stamped filenames.
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    MIT
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    A Model Context Protocol server for managing prompt templates as markdown files with YAML frontmatter, allowing users and LLMs to easily add, retrieve, and manage prompts.
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    A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.
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    Apache 2.0
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    Enables token-efficient semantic search and analysis over any directory of files through hybrid search, directory overview, structural analysis, and dependency graphs.
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    MIT
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    Enables Claude Code to interact with Jupyter notebooks, perform semantic search over knowledge files, and manage research projects.
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    Enables SQL querying over CSV and Excel files using DuckDB, providing tools to load files, inspect schemas, and run read-only queries via MCP.
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    MIT
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    Provides intelligent retrieval capabilities for local files by scanning directories, generating vector indexes, and enabling semantic search through RAG (Retrieval Augmented Generation) with incremental indexing support.
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