Convert web pages to clean markdown format by extracting content, removing unnecessary elements, and ranking information for RAG applications.
MIT
458,064 tools. Updated 2026-08-14 22:15
"Agentic RAG: Understanding or Exploring Its Meaning and Applications" matching MCP tools:
- Find code by meaning using semantic search with natural language queries. Combines AI understanding with text matching to locate relevant snippets, handling typos and variations.MIT
- Search code with natural language queries to locate relevant snippets by meaning or exact match, even with typos or variations.MIT
- Recall relevant memories by meaning with RAG. Search across global, project, or custom memory buckets to find contextual knowledge from past sessions.MIT
- Convert skill directories into Weaviate-ready format for hybrid vector and keyword search. Enables production RAG applications with BM25 and vector retrieval.MIT
- Generate text embeddings to enable semantic search and retrieval-augmented generation (RAG) for AI applications.MIT
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- Alicense-qualityAmaintenanceEnables persistent, searchable memory for Claude Code by storing session knowledge in a local PostgreSQL + pgvector database, with hybrid vector and full-text search, automatic session mining, and knowledge graph curation.MIT
- Flicense-qualityBmaintenanceAn MCP-based multi-agent retrieval-augmented generation system that enables question answering over academic papers with hybrid search, knowledge graph multi-hop reasoning, and source-cited answers.
Matching MCP Connectors
RAG-as-a-service MCP sunucusu — çok-kiracılı koleksiyon yönetimi, metin ingest (chunk+embed+upsert,…
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- Discover available knowledge bases for Retrieval-Augmented Generation (RAG) workflows. Lists all document repositories to enable integration into AI applications.MIT
- Analyze any text or phenomenon through 8 integrated hermeneutic lenses to decode meaning and understand multiple perspectives.
- Convert text into semantic embeddings for similarity search, clustering, and RAG applications using Saptiva AI's embedding model.
- Generate search results optimized for RAG context by providing relevant web content based on your query, with adjustable depth and result limits.
- Conducts research by orchestrating graph RAG, hypothesis generation, and synthesis tasks through a unified interface.MIT
- Create a RAG knowledge base to enable retrieval-augmented generation. Returns the new knowledge base ID for immediate use in your applications.
- Fetches the full text of a methodology document using its exact agentic-os:// URI. Use after search_methodology to retrieve document content.Apache 2.0
- Search agentic-os governance, SDLC pipeline, and QE blueprint documentation using full-text queries. Every term must appear in a document for a match; use distinctive words for better results.Apache 2.0
- List all applications owned by your account to check existing ones or retrieve an applicationId before creating new applications.MIT
- Find relevant Redis documentation and knowledge base articles by asking questions about concepts, data structures, features, and common use cases like caching, rate limiting, and RAG.MIT
- Convert text into a numeric vector for semantic search, RAG, and similarity. Supports multiple languages.MIT
- Retrieve a blueprint summary by ID, including title, executive summary, agentic pattern, platform, agent roles, and phase overview.MIT
- Executes a multi-step agentic research task for open-ended questions, polls until completion, and returns a summarized thread.MIT
- Convert any public web page into token-efficient Markdown for RAG and AI agents. Optionally render JavaScript for single-page apps.MIT