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"Using Google Sheets, Google Drive, and Google Docs" matching MCP servers:

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    Enables AI agents to search local Markdown documents using natural language, with automatic indexing and section-level retrieval.
    9
    2
    1
    MIT
  • A
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    B
    maintenance
    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
  • A
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    B
    maintenance
    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
  • A
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    Provides semantic search over ScriptingApp documentation by converting Markdown/MDX files into a LlamaIndex vector store. Supports multi-language indexing and enables natural language queries against technical documentation through MCP tools.
    2
  • A
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    quality
    D
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    Enables semantic search and retrieval over local Markdown/MDX documentation using Node.js-based embeddings. Supports multi-language documentation with offline vector indexing and MCP tool exposure for AI assistants.
    32
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    MIT
  • A
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    Enables AI assistants to search and retrieve information from Teleport documentation using a vector database. It provides a tool for semantic vector search over pre-populated embeddings of Teleport pages and examples to assist with technical queries.
    1
    MIT
  • F
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    quality
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    maintenance
    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.
  • F
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    quality
    C
    maintenance
    An MCP server that answers questions over insurance and regulatory documents using retrieval-augmented generation, returning grounded, cited passages via local embeddings and OpenSearch.
  • F
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    quality
    D
    maintenance
    Enables semantic search and question-answering over uploaded documents using vector embeddings and Google AI. Supports document organization with tags, section-aware queries, and hierarchical markdown structure preservation.
  • -
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    quality
    C
    maintenance
    Enables LLM hosts to retrieve live, relevant documentation excerpts from official library docs sites via a search-and-RAG tool, avoiding reliance on training data.
  • A
    license
    A
    quality
    C
    maintenance
    An MCP server that provides AI assistants with access to Multi Theft Auto: San Andreas function documentation through vector similarity search and smart keyword expansion. It enables efficient information retrieval with features like deprecation warnings and SQLite caching for technical documentation.
    11
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    GPL 3.0
  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.
    MIT
  • A
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    Not graded
    quality
    D
    maintenance
    A vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.
    2
    MIT
  • A
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    Not graded
    quality
    D
    maintenance
    Enables Claude to search and retrieve documents from Azure AI Search indexes with intelligent summarization and analysis using LangGraph workflows and optional Google Gemini integration.
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    An MCP server providing semantic memory storage and retrieval using vector embeddings powered by LanceDB and Google Gemini. It supports multi-tenant isolation and bucket-based organization for managing structured memories through natural language queries.
    MIT
  • F
    license
    A
    quality
    A
    maintenance
    Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.
    10
    9