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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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    Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.
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    MIT
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    Offline AI-powered local file search MCP server for Windows. Searches inside document contents (Word, Excel, PDF, PowerPoint, HWP) using BM25 + dense vector hybrid search. 100% local, no cloud, no login, no telemetry.
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    Apache 2.0
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    Upload documents (Word, Excel, PDF, PowerPoint) to a vector RAG store and perform semantic search with page-level citations. Queries are free; ingestion costs credits at break-even pricing.
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    MIT
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    MCP server for indexing, semantic search, and generation of multi-format documents. Exposes 13 tools over JSON-RPC 2.0 so an LLM can search your local PDF, Excel, and Word files, and create or edit Excel and Word documents.
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    AGPL 3.0
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    Imagine you could hand Claude a filing cabinet of your documents and say "remember all of this." Later you just ask questions, and Claude answers from what it remembers — citing which document each fact came from. That's Memorised them All. It's a small add-on (an MCP server) for Claude Desktop and Claude Code that: Reads your files — PDFs, Word/Excel/PowerPoint, web pages, images (with OCR), ev
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    MIT
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    A lightweight document parser MCP server that enables Claude to parse PDFs, Word, Excel, images (OCR), and other document formats with support for chunking and metadata extraction.
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    MIT
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    Enables AI to efficiently analyze and extract structured data from complex documents, especially Excel files, by providing tools like section reading and field mapping to reduce token usage and improve success rates.
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    MIT
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    Enables AI assistants to parse and search documents including PDF, Word, Excel, PowerPoint, and images via OCR, with support for semantic search and batch processing.
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    MIT
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    RAG-powered document search server that enables semantic search across large collections of legal and business documents (PDF, Word, Excel, PowerPoint) using local embeddings with no API costs.
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    MIT
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    A Model Context Protocol server that enables intelligent task delegation from advanced AI agents like Claude 3.7 to cost-effective LLMs, providing a comprehensive suite of tools spanning cognitive memory, browser automation, Excel manipulation, database interactions, and document processing.
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    Retrieval-augmented MCP server for company project documents (Markdown, Word, Excel, PDF) with vector search, offline embedding, and token-based project authorization.
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    Converts Excel table definitions to structured JSON and exposes them to LLMs via MCP tools like list_tables and get_table_schema, enabling accurate SQL generation and data modeling assistance.
    Last updated