knowledge-to-action-mcp and mcp-rag-server
These two projects appear to be competing implementations of an MCP server designed for Retrieval Augmented Generation, both aiming to provide context to LLMs from user-provided documents.
About knowledge-to-action-mcp
tac0de/knowledge-to-action-mcp
MCP server for Obsidian GraphRAG, agent-ready context, preview-only planning, and safe repo handoffs
Combines graph-aware note retrieval with optional embeddings-based semantic reranking to surface contextual neighbors, then structures results into agent-ready packets containing briefs, risks, and repo file hints. Implements preview-only action planning and bounded workspace inspection (ripgrep, git status) without exposing shell access, integrating via stdio with Claude, VS Code, and Cursor.
About mcp-rag-server
kwanLeeFrmVi/mcp-rag-server
mcp-rag-server is a Model Context Protocol (MCP) server that enables Retrieval Augmented Generation (RAG) capabilities. It empowers Large Language Models (LLMs) to answer questions based on your document content by indexing and retrieving relevant information efficiently.
This is a tool for developers who integrate large language models (LLMs) into applications. It takes your collection of documents, like text files or markdown, and turns them into a searchable index. This index then helps your LLM provide more accurate and context-aware answers based on your specific content, rather than just its general training data.
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