Check if the agentic-sidecar is importable and verify that its decision runtime is ready for operation.
MIT
649,985 tools. Updated 2026-10-12 19:07
"Agentic RAG: Understanding or Exploring Its Meaning and Applications" matching MCP tools:
- Convert web pages to clean markdown format by extracting content, removing unnecessary elements, and ranking information for RAG applications.MIT
- 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
- Get the current agentic session state — failure streak, recent errors, intent chain. Useful for understanding context before deciding next action.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
Matching MCP Servers
- AlicenseNot gradedqualityAmaintenanceEnables 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
- AlicenseNot gradedqualityBmaintenanceEnables cited, revision-aware retrieval over real IETF RFC chunks with zero LLM in the answer path, including graph-based supersession resolution and relationship queries via MCP tools.MIT
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- Generate text embeddings to enable semantic search and retrieval-augmented generation (RAG) for AI applications.MIT
- 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.-
- 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
- Checks connectivity to agenticlens, agentic-chaos, agentic-sidecar, and ai-operations-spec, and reports readiness. Detects unreachable services to prevent workflow failures.MIT
- List scaffolded modules, framework adapters, and integration placeholders currently present in agentic-sidecar to reveal project structure and identify missing pieces.MIT
- Reduce a .docx to a target word count while preserving meaning, structure, and citations. Saves the condensed file next to the original and returns its path.Apache 2.0
- Find document types semantically similar to a query via multilingual semantic search. Matches by meaning, not exact text, using RAG-based embeddings.MIT
- Confirm that the dryad-rag-pipeline service is reachable to ensure agents can reliably index and search documents.MIT