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"Definition and meaning of the word 'word'" matching MCP servers:

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    An enterprise-ready MCP server that exposes a RAG tool for retrieving relevant context and metadata from a Qdrant vector database using natural language queries.
    2
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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.
    MIT
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    Enables real-time indexing and semantic search of local documents (PDF, Word, text, Markdown, RTF) using vector embeddings and local LLMs. Monitors folders for changes and provides natural language search capabilities through Claude Desktop integration.
    22
    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.
    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.
    4
    MIT
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    A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.
    3
    3
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    Enables semantic search and analysis of customer support tickets. Provides tools to search tickets, analyze the dataset, and retrieve individual tickets using natural language.
    3
    MIT
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    An MCP server that enables RAG-powered AI chat integration for websites by crawling content, building local vector stores, and generating embeddable chat widgets. It simplifies the setup of local chat servers with support for various LLM and embedding providers.
    5
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    MIT
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    ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a conceptual fork of OrientDB, the first Multi-Model DBMS. ArcadeDB supports Vector Embeddings.
    10
    1,090
    Apache 2.0
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    Headless geometric memory engine for AI agents — no Vector DB, no cloud, no API key. Store and retrieve by meaning using native Vector Symbolic Architecture (NVSA) math over O_DIRECT NVMe mapping. Runs entirely on your machine via MCP.
    87
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    AGPL 3.0
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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.
    3
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    Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.
    5
    18
    MIT
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    A lightweight server implementation of the Model Context Protocol that connects Memgraph database with LLMs, allowing users to interact with graph databases through natural language.
    1
    25
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    Model Context Protocol (MCP) server for TigerGraph that lets AI agents interact with TigerGraph through the MCP standard using pyTigerGraph's async APIs.
    3
    Apache 2.0
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    MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.
    16
    MIT
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    An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.
    16
    MIT