Retrieval

RAG: the pages it generated

Retrieval-augmented generation over this dealer's data. Every vehicle and store page is expanded into plain-text Markdown you can browse below, pull as one bulk corpus, or query live with the rag_search tool.

How the RAG corpus works

Each vehicle and store page is written out as clean Markdown and concatenated into a single bulk corpus, llms-full.txt (657 KB). An agent can ingest the whole corpus, or call rag_search(query) over the MCP endpoint to retrieve just the matching chunks with source links. Everything below is a real generated page you can open.

642vehicle pages (.md)
1store pages (.md)
657 KBbulk corpus

All 642 vehicles are in the corpus as one Markdown record per VIN, addressed by the pattern https://boballenmotormall.ai/v/{VIN}.md (and https://boballenmotormall.ai/v/{VIN}.json). A few samples:

How to query this dataset
Bulk corpus
Live tool
rag_search(query, limit?) over the MCP endpoint
Per vehicle
https://boballenmotormall.ai/v/{VIN}.md
curl
curl -s -X POST https://boballenmotormall.ai/api/ucp/mcp \
  -H 'content-type: application/json' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"rag_search","arguments":{}}}'