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    A Model Context Protocol (MCP) server for AWS S3 integration using FastMCP and boto3. This server provides access to S3 functionality through MCP-compatible tools.
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    MIT
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    Enables AI assistants to search and query PDF documents through a local RAG system with vector embeddings. Provides semantic document search capabilities while keeping all data stored locally without external dependencies.
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    Enables AI agents to semantically search GitHub repository documentation by automatically fetching, vectorizing, and indexing content into an Upstash Vector database. It provides a standard MCP interface for agents to retrieve relevant documentation snippets through natural language queries.
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    An Amazon S3 Model Context Protocol server that allows Large Language Models like Claude to interact with AWS S3 storage, providing tools for listing buckets, listing objects, and retrieving object contents.
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    MIT
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    A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.
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    Apache 2.0
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    Enables retrieval-augmented generation (RAG) by indexing and searching through documents (Markdown, text, PowerPoint, PDF) using vector embeddings with multilingual-e5-large model and PostgreSQL pgvector. Supports contextual chunk retrieval and incremental indexing for efficient document management.
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    MIT
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    Enables interaction with AWS S3 through MCP, supporting bucket and object management, lifecycle configurations, tagging, policies, CORS settings, presigned URLs, and file uploads/downloads.
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    MIT
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    A secure vector-based memory server that provides persistent semantic memory for AI assistants using sqlite-vec and sentence-transformers. It enables semantic search and organization of coding experiences, solutions, and knowledge with features like auto-cleanup and deduplication.
    Last updated
    MIT