What does the end user do with the AI chat? It sounds like they can just use it to do searches of client information… which the existing site would already do.
Was there any concern about giving the LLM access to this return data? Reading your article I wondered if there could be an approach that limits the LLM to running the function calls without ever seeing the output itself fully, e.g., only seeing the start of a JSON string with a status like “success” or “not found”. But I guess it would be complicated to have a continuous conversation that way.
If all this does is give you the data from a contact API, why not just let the users directly interact with the API? The LLM is just extra bloat in this case.
Surely a fuzzy search by name or some other field is a much better UI for this.
I built a similar system for php and I can tell you what is the smart thing here: accessing data using tools.
Of course tool calling and MCP are not new. But the smart thing is that by defining the tools in the context of an authenticated request, one can easily enforce the security policy of the monolith.
In my case (we will maybe write a blog post one day), it's even neater as the agent is coded in Python so the php app talks with Python through local HTTP (we are thinking about building a central micro service) and the tool calls are encoded as JSON RPC, and yet it works.
- Made a RAG in ~50 lines of ruby (practical and efficient)
- Perform authorization on chunks in 2 lines of code (!!)
- Offload retrieval to Algolia. Since a RAG is essentially LLM + retriever, the retriever typically ends up being most of the work. So using an existing search tool (rather than setting up a dedicated vector db) could save a lot of time/hassle when building a RAG.
Surely a fuzzy search by name or some other field is a much better UI for this.
Of course tool calling and MCP are not new. But the smart thing is that by defining the tools in the context of an authenticated request, one can easily enforce the security policy of the monolith.
In my case (we will maybe write a blog post one day), it's even neater as the agent is coded in Python so the php app talks with Python through local HTTP (we are thinking about building a central micro service) and the tool calls are encoded as JSON RPC, and yet it works.
- Made a RAG in ~50 lines of ruby (practical and efficient)
- Perform authorization on chunks in 2 lines of code (!!)
- Offload retrieval to Algolia. Since a RAG is essentially LLM + retriever, the retriever typically ends up being most of the work. So using an existing search tool (rather than setting up a dedicated vector db) could save a lot of time/hassle when building a RAG.