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Search local documents with vector similarity for RAG answers.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "rag_mcp" yet — see the docs or source repo.
Answer questions about “liability for breach” based on contract clauses in local ./docs, and list sources.
An accurate answer grounded in retrieved documents with cited sources.
Search local PDFs, DOCX files, and Markdown notes, then summarize key project requirements and action items.
A cross-format retrieval result with summaries and action items.
Search the local paper folder for passages about “vector databases,” then summarize main ideas, consensus, and disagreements.
A structured literature review based on retrieved passages.
Search locally indexed PDF docs with citations and context for LLM agents.
Store and retrieve text semantically with local vector memory for conversations.
Retrieve and process docs with vector search to enrich AI responses.
Build and query vector knowledge bases for semantic search and RAG workflows.
Index and semantically search code, PDFs, and documents with exact citations.
Retrieve relevant document chunks and generate suggested LLM prompts via REST and MCP.