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"Working with Excel" matching MCP servers:

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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 LLM access to Exasol database via MCP tools, including metadata reading and data queries, with an optional on-premise text-to-SQL (Governed SQL) translation.
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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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    A Model Context Protocol server integration that creates a persistent, searchable working memory for AI-assisted development by enabling automated context recall and knowledge persistence in Chroma, the open-source embedding database.
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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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    Enables LLMs to perform sophisticated web searches through proxy servers using Tavily's API, supporting comprehensive web searches, direct question answering, and recent news article retrieval with AI-extracted content.
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    MIT
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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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    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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    A Model Context Protocol server that exposes multiple AI tools over SSE transport with JWT-based secure authentication, allowing for dynamic tool registration and session management.
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    MIT
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    Upload Word, Excel, PDF, or PowerPoint documents to a vector RAG store with vision-model extraction, then search semantically and retrieve chunks with page numbers for precise citations.
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    MIT
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    Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.
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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