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"Generate Word documents" matching MCP servers:

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    Enhances developer tasks by retrieving relevant context from project documentation via a backend API. It enables users to generate enriched task descriptions and prompts by automatically integrating specific technical details from their project documents.
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    MIT
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    Enables LLMs to create, query, and manage MongoDB-compatible databases using natural language without actual data storage. Uses statistical modeling to generate realistic data on-the-fly from sample documents or descriptions.
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    A demonstration server implementing the Model Context Protocol (MCP) with both STDIO and Server-Sent Events (SSE) support, featuring a word reversal tool that transforms input text by reversing characters.
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    MIT
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    A Node.js-based MCP server that enables AI agents to generate embeddings, index documents, and perform semantic vector searches using OpenAI and Chroma. It facilitates the creation of retrieval-augmented generation (RAG) pipelines for internal knowledge assistants and document-based workflows.
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    Enables AI agents to search, deep-read, and build knowledge bases from Markdown, PDF, DOCX, and PPTX documents via MCP tools for retrieval, document navigation, and ingestion.
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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 narrative graph engine that enables LLMs to generate, track, and mutate complex fictional worlds while maintaining consistency between factions, characters, and locations. It acts as a specialized RAG framework for storytelling, allowing models to manage thousands of entities without exceeding context limits.
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    MIT
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    A personal board of AI advisors grounded in public-domain texts, with a fail-closed fidelity contour: every quote is checked word-for-word against its source by code, or the advisor abstains. Runs locally as a stdio MCP server.
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    MIT
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    Enables secure access to legal documents from Clio via Claude Desktop, with local processing and semantic search to ground AI responses in actual documents.
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    Mozilla Public 2.0
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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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    A vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.
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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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    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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    An MCP server that retrieves relevant code snippets and documents to assist in generating pymilvus code, converting between ORM and client, and translating Milvus code between programming languages.
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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