R2R MCP. Query your private knowledge base directly from your AI client.

This server connects your AI agent directly to your private knowledge base. You can run vector searches and execute precise Retrieval-Augmented Generation (RAG) queries without ever leaving your chat interface. It lets your agent ask questions against proprietary data—not just general web knowledge.

Checking the System Status

You start by verifying everything's running right with get_health. You call this tool to ping the R2R server, confirming its operational status and making sure it’s ready for vector operations. It gives you a clean confirmation that your connection is good to go.

Mapping Out Your Data Inventory

Before querying anything, you gotta know what data you're working with. You use list_collections when you need a rundown of all the distinct, organized document groups available in the entire system. This shows you the top-level categories of your knowledge. Next, if you select a specific collection, you run list_documents.

This tool lists metadata and IDs for every ingested file within that selected grouping, giving you an inventory count and basic details for everything stored.

If you need to pull the full context—the actual content and deep metadata for one specific piece of writing—you use get_document, passing in a known document ID. This retrieves all the detailed information attached to that single file record.

Performing Searches and Generating Answers

You've checked the inventory; now you need answers. For a foundational understanding, you can run a semantic search using the search tool. It performs a deep similarity search across every ingested document, finding contextually relevant chunks of text based on meaning, not just matching keywords. This is how your agent figures out what's related to what.

When you need more than just a few snippets, you run advanced RAG queries using rag_query. This specialized tool executes the full Retrieval-Augmented Generation process, summarizing answers directly from multiple retrieved vector data chunks. It gives your agent a synthesized answer based on deep knowledge extraction, which is way better than simple search results.

How You Use It in Practice

Your AI client calls one of these tools—like list_collections to see what's available first, or search when you know the topic. The R2R server processes that request against your private vector store and sends back the clean data, context, or summarized answer right into your chat workflow for final use by your agent.

It handles the complexity of proprietary data retrieval so you don't have to.

You control the entire process: checking status with get_health, scoping down your files using list_collections and then list_documents, pulling specific texts via get_document, finding relevant passages with search, or generating a comprehensive answer using rag_query. It keeps all this powerful, private data management right inside the chat interface.