Create a named document collection to group related evidence bundles for cross-document semantic search and RAG-based Q&A.
MIT
476,070 tools. Updated 2026-08-25 05:27
"Information or uses related to a rag" matching MCP tools:
- Synchronize project folders from Google Drive to RAG, using Drive as the master to add or remove projects.MIT
- Ask natural-language questions about your browsing history and get AI-powered answers using RAG. Filter results by event type, domain, or time window.MIT
- Retrieve relevant information from RAG and Wiki sources using keyword, semantic, hybrid, or auto search strategies.MIT
- Recommend related academic papers by supplying seed paper IDs. Uses Semantic Scholar to discover relevant research.MIT
- Ask questions with RAG-enhanced context from xAI Collections, using cached retrieval for faster repeated queries.MIT
Matching MCP Servers
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.4451MIT
- FlicenseNot gradedqualityBmaintenanceEnables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.4
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- Retrieve detailed information about a specific RAG project within the Calibre ebook library, including its configuration, contents, and organization for semantic search and contextual conversations.
- Convert a website or up to 1000 URLs into RAG-ready chunked JSONL for vector database import.MIT
- Keep RAG search current by re-indexing catalog schema after changes. Uses schema hashing to skip unchanged schemas.Apache 2.0
- Split public webpages into deterministic, bounded RAG chunks for AI retrieval. Respects access controls, uses cached results by default, and rejects SSRF-sensitive URLs.Apache 2.0
- Retrieve similar rows in Postgres using pgvector k-NN and expand each hit with related parent and child records via foreign keys, providing context for RAG without an embedding model.MIT
- Re-index markdown documents to refresh the RAG search index. Use before querying when the index is empty or source files change.MIT
- Convert text into a numeric vector for semantic search, RAG, and similarity. Supports multiple languages.MIT
- Retrieve detailed information about any Apify Actor by ID or full name. Select which fields to return—such as input schema, README, pricing, or MCP tools—to get exactly the documentation you need.MIT
- Retrieve a list of all knowledge folders in your organization, each containing documents for RAG capabilities.MIT
- Perform semantic search over a stored memory namespace to retrieve the most relevant entries. Use this to recall context for RAG pipelines.MIT
- Check the current status of the RAG documentation index, including total chunks, configured sources, and last update times.Apache 2.0
- Get precise answers to any question about Lamatic.ai documentation by searching all indexed docs with RAG.MIT
- Search the internet using Brave Search API or DuckDuckGo to find relevant information for research tasks.MIT
- Search engrams by cosine similarity to classify duplicates, related, or new content. Uses score thresholds: >0.9 duplicate, 0.7-0.9 related, <0.7 new.Apache 2.0