List documents in a Needle collection to check processing status, inventory available files, and verify document availability before searching.
MIT
223,854 tools. Last updated 2026-06-22 05:54
"Information on RAG Documents or Processing" matching MCP tools:
- Retrieve statistical metrics about a collection's documents, processing status, storage usage, and index health to monitor readiness and verify completion.MIT
- Retrieve a list of all knowledge folders in your organization, each containing documents for RAG capabilities.MIT
- Retrieve OCR processing statistics: total documents, counts by state (completed, not processed, processing, error), and per-folder breakdown.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
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- 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
- Find documents based on their position in a folder hierarchy by providing keywords for each path level. Returns matching document filings.Apache 2.0
- 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 a simplified list of documents and document groups by status, sort order, folder, or expiration filter.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