Retrieve statistical metrics about a collection's documents, processing status, storage usage, and index health to monitor readiness and verify completion.
MIT
404,457 tools. Last updated 2026-08-06 21:51
"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
- Create a knowledge store to store documents for RAG, enabling AI agents to search and answer questions. Set dryRun=false to create.MIT
- Retrieve a list of all knowledge folders in your organization, each containing documents for RAG capabilities.MIT
- Index local research documents (PDF, Markdown, JSON, TXT) into the Onyx RAG knowledge base. These documents become searchable for instant retrieval and querying.MIT
- List documents and web pages ingested into a knowledge store for RAG retrieval, with pagination and filtering.MIT
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- Alicense-qualityAmaintenanceEnables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.Last updated471MIT
- Alicense-qualityDmaintenanceA 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 updated7MIT
Matching MCP Connectors
Congressional Documents — full-text search and retrieval over the official
Image processing for AI agents. Resize, convert, compress, and pipeline images.
- Retrieve relevant information from RAG and Wiki sources using keyword, semantic, hybrid, or auto search strategies.MIT
- List all ingested source documents with their licenses, URLs, and processing states to review the corpus contents.MIT
- Analyze large documents by breaking them into chunks and processing in parallel with multiple AI models to summarize, extract information, answer questions, or search content.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
- Scans document text, source, and type to validate and prepare content for retrieval-augmented generation (RAG) ingestion.Apache 2.0
- Analyze document content using AI to bulk assign relevant tags. Skip already tagged documents and control processing volume.
- Create a named local vector index for retrieval-augmented generation. Documents added are embedded via Ollama for local RAG without cloud dependencies.
- Add documents to a collection by providing a URL for download, processing them for text extraction, and indexing them for semantic search.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
- Scrape a public URL to create a searchable knowledge base. Extracts content from web pages (including JavaScript-rendered), chunks it, and indexes for RAG.MIT
- Retrieve metadata and processing state for a track's stereo audio, Dolby Atmos audio, or lyrics file. Returns file information without the file bytes.MIT
- List existing knowledge bases (documents, FAQs, web content) to prevent duplicates and find knowledge_base_ids for agent attachment.MIT