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"Managing and Working with Files" matching MCP servers:

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    A very simple vector store that provides capability to watch a list of directories, and automatically index all the markdown, html and text files in the directory to a vector store to enhance context.
    17
    42
    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.
    1
    MIT
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    Provides AI assistants with long-term semantic memory capabilities through local vector-based storage. Enables storing, recalling, and managing information across sessions with complete privacy using ChromaDB, with no data ever leaving your machine.
    3
    9
    MIT
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    Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.
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    360
    MIT
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    Integrates Redshift database query capabilities with vector-based knowledgebase tools for semantic search and RAG applications. It enables users to execute SQL queries, explore database schemas, and perform hybrid semantic searches on markdown files stored in S3.
    7
    MIT
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    A Model Context Protocol server that allows Large Language Models to interact with Astra DB databases, providing tools for managing collections and records through natural language commands.
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    Apache 2.0
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    An MCP server for OpenServerless that exposes action tools for creating, invoking, and managing API endpoints with integrated services like S3, PostgreSQL, Redis, and Milvus.
    8
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    Apache 2.0
  • F
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    A lightweight, local-first MCP server that automatically watches folders, chunks and embeds files using Transformers.js, and exposes semantic search capabilities to VS Code and Cursor. Runs completely offline with SQLite vector storage, designed for resource-constrained environments.
    4
    1
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    An MCP server for querying and managing LlamaIndex documents stored in Qdrant vector databases, with automatic embedding model detection and extensive tools for search, retrieval, and collection management.
    17
    Apache 2.0
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    Provides persistent memory and knowledge graph capabilities for AI assistants using local SQLite storage. Enables creating, searching, and managing entities, relationships, and observations with vector search support across conversations.
    7
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    14
    MIT
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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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    An interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.
    188
    MIT
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    Local-first MCP server for long-term memory of dev sessions. It indexes session metadata with embeddings in Postgres/pgvector, enables semantic search over past sessions, and resolves only relevant session files for context.
    21
    MIT
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    A high-performance FastAPI server supporting Model Context Protocol (MCP) for seamless integration with Large Language Models, featuring REST, GraphQL, and WebSocket APIs, along with real-time monitoring and vector search capabilities.
    8
    MIT