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223,854 tools. Last updated 2026-06-22 05:54

"Information on RAG Documents or Processing" matching MCP tools:

  • List documents in a Needle collection to check processing status, inventory available files, and verify document availability before searching.
    MIT
  • Search uploaded documents using RAG to find answers with citations. Ask questions to retrieve information from your knowledge base.
    MIT
  • Find relevant documents in the RAG system using semantic search with customizable similarity thresholds and result limits.
    MIT

Matching MCP Servers

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    A production-ready Model Context Protocol server that bridges local document management with cloud synchronization (Notion) for AI agent integration, enabling seamless access and sync of local and cloud documents.
    Last updated
    5
    MIT

Matching MCP Connectors

  • ship-on-friday MCP — wraps StupidAPIs (requires X-API-Key)

  • Image processing for AI agents. Resize, convert, compress, and pipeline images.

  • Create a named local vector index for retrieval-augmented generation. Documents added are embedded via Ollama for local RAG without cloud dependencies.
  • Extract structured data from documents using custom or auto-generated schemas to process various file formats including PDF, images, and Office documents.
    MIT
  • Fetch detailed Actor information by ID or full name, including description, input schema, statistics, and pricing. Control returned fields via the 'output' parameter.
    MIT
  • Execute a complete RAG workflow to answer questions using retrieved context documents. Handles embedding, semantic search, and answer generation with direct quotes.
    MIT
  • Filter documents by metadata before ranking by vector similarity to enable production RAG and semantic search pipelines.
    MIT
  • Retrieve technical metadata from a Cloudglue video file including duration, resolution, file size, and processing status. Use when you need video specifications instead of content analysis.
    MIT
  • Retrieve detailed form template information to examine structure and field specifications before creating documents or new forms in RSpace.
    AGPL 3.0
  • Add documents to a collection by providing a URL for download, processing them for text extraction, and indexing them for semantic search.
    MIT
  • Generate vector embeddings from text for semantic search, RAG, clustering, or similarity tasks. Choose between query or document input type and adjust model quality and dimensionality.
    MIT
  • Search documents using semantic understanding to find relevant content based on meaning rather than keywords. Understands natural language queries and returns ranked passages with source information.
    MIT
  • Retrieve detailed information about a media file, including type, dimensions, and upload status. Use this to verify processing completion before attaching media to a post.
    MIT
  • Sort documents by their relevance to a specific query using Jina AI's reranking technology. Organize search results or content collections to prioritize information that best matches your topic.
    Apache 2.0