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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.
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    Apache 2.0
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    Integrates R2R (Retrieval-Augmented Generation) with Claude Desktop, enabling semantic search across knowledge bases and RAG-based question answering with support for vector, graph, web, and document search.
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    Enables AI agents to manage and search data in Redis using natural language. Supports hashes, lists, sets, sorted sets, streams, JSON, and vector search.
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    MIT
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    Enables Claude to store and query personal finance transactions using semantic search. Transactions are persisted in ChromaDB and JSON, allowing natural language questions about spending trends and portfolio allocations.
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    MIT
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    A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.
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    MIT
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    Enables AI-powered analysis of Ethereum blockchain data through semantic search, natural language queries, and structured filtering. Provides comprehensive access to addresses, transactions, blocks, tokens, and smart contracts with real-time blockchain intelligence.
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    MIT
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    Semantic, on-demand skill retrieval for Claude Code that saves tokens and improves skill discovery by replacing the native skill listing with vector embedding search.
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    MIT
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    Provides advanced document search and processing capabilities through vector stores, including PDF processing, semantic search, web search integration, and file operations. Enables users to create searchable document collections and retrieve relevant information using natural language queries.
    MIT
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    Enables private, offline semantic search across local files (documents, images, videos) using OCR and vector search, and optionally performs web research with cited sources.
    AGPL 3.0
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    A general-purpose MCP memory server that gives AI clients long-term memory with semantic search, memory classification, priority management, and progressive injection, supporting local JSON or cloud PostgreSQL storage.
    AGPL 3.0
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    A database-agnostic MCP server that integrates web crawling with Retrieval Augmented Generation, supporting multiple AI providers and vector database backends for flexible and intelligent content retrieval.
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    MIT
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    Open-source MCP servers for ESG data extraction, analysis, and regulation management, providing 31 tools across 6 servers for tasks like metrics extraction, PDF processing, vector storage, and web scraping.
    MIT
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    Enables AI-powered semantic search and question-answering for LiveKit documentation using Pinecone vector search and real-time web search with Tavily, providing detailed responses with source attribution.