Search tool calling is RAG. Maybe we should call it a "RAG Agent" to be more en vogue heh. But RAG is not just similarity search on embeddings in vector DBs. RAG is any type of a retrieval + context injection step prior to inference.

Heck, the RAG Agent could run cosign diff on your vector db in addition to grep, FTS queries, KB api calls, whatever, to do wide recall (candidate generation) then rerank (relevance prioritization) all the results.

You are probably correct that for most use cases search tool calling makes more practical sense than embeddings similarity search to power RAG.

How would you use tool-calling to filter through millions of documents? You need some search functionality, whether old-school search or embedding search. If you have only thousands of documents, then sure, you don't need search, as you can feed them all to the LLM.

Tool calling complements RAG. You build a full scale RAG (embedding, reranker, create prompt, get output from LLM) and hook that to a tool another agent can see. That combines both their power.