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"Kingsoft Document Connector" matching MCP servers:

  • A
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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.
    9
    3,271
    365
    MIT
  • A
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    Enables LLMs to interact with Zvec vector database through tools for collection management, document operations, vector search, and AI-powered embeddings.
    17
    7
    Apache 2.0
  • F
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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.
    2
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    A server that enables vector and keyword search capabilities in Typesense databases through the Model Context Protocol, providing tools for collection management, document operations, and search functionality.
    14
    10
    MIT
  • A
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    Enables AI assistants to interact with Meilisearch through a standardized interface, supporting index and document management, search capabilities, settings configuration, task monitoring, and experimental vector search.
    68
  • F
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    A server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.
    3
  • A
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    quality
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    Enables AI agents to interact with INFINI Easysearch (compatible with Elasticsearch/OpenSearch APIs) through 121 tools covering cluster management, index operations, document manipulation, search queries, snapshots, and monitoring.
    100
    4
    MIT
  • A
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    quality
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    A Model Context Protocol server for indexing and searching local document collections with hybrid BM25+vector search, optimized for Korean and English, supporting DOCX, PDF, audio, and more.
    MIT
  • A
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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
  • A
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    Enables AI assistants to search and retrieve information from your knowledge base using RAG (Retrieval-Augmented Generation) with hybrid search, document indexing, and ChromaDB vector storage.
    115
    MIT
  • A
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    A Retrieval Augmented Generation system that enables AI assistants to perform semantic searches and manage document indices for markdown files. It supports PostgreSQL with pgvector and integrates both Google Gemini and Ollama for intelligent embedding generation.
    1
    MIT
  • A
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    Enables AI assistants to interact with MariaDB databases through standard SQL operations and advanced vector/embedding-based search. Supports database management, schema inspection, and semantic document storage and retrieval with multiple embedding providers.
    MIT
  • A
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    Exposes LangChain and Anthropic Claude capabilities as tools for generating production-ready RAG systems, Supabase vector stores, and document ingestion pipelines. It enables users to instantly scaffold AI infrastructure and document processing code through natural language prompts in MCP-compatible clients.
  • A
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    A minimal RAG service that exposes a vector index for document retrieval via REST and MCP, allowing querying for relevant document chunks and returning a suggested LLM prompt.
    MIT