Convert web pages to clean markdown format by extracting content, removing unnecessary elements, and ranking information for RAG applications.
MIT
192,851 tools. Last updated 2026-06-11 10:59
"Agentic RAG: Understanding or Exploring Its Meaning and Applications" matching MCP tools:
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- Convert text into semantic embeddings for similarity search, clustering, and RAG applications using Saptiva AI's embedding model.
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- Generate search results optimized for RAG context by providing relevant web content based on your query, with adjustable depth and result limits.
- Retrieve relevance-ranked web content with actual page text, tables, and code for AI grounding and RAG pipelines.
- Discover code by meaning and structural relationships using hybrid semantic and graph search for initial codebase exploration.MIT
- List all database schemas to understand database organization before exploring tables. Optionally retrieve tables within each schema in a single call.Apache 2.0
- Execute a complete RAG workflow to answer questions using retrieved context documents. Handles embedding, semantic search, and answer generation with direct quotes.MIT
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- Map coded column values to their business meanings, enabling natural language understanding of database content.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 a blueprint summary by ID, including title, executive summary, agentic pattern, platform, agent roles, and phase overview.MIT
- Retrieve a list of all knowledge folders in your organization, each containing documents for RAG capabilities.MIT
- Withdraw a submitted job application. Requires confirmation as action is irreversible for accepted or rejected applications.MIT
- Combine BM25 keyword search with vector ANN search in a single pass. Use for RAG when either semantic or keyword search alone is insufficient.MIT