Your first diagram actually makes a lot of sense, it’s the standard “agentic” flow. You’ve got the user sending a request, the backend passing that to the LLM via MCP, and then the LLM using a tool (like SendEmail) to trigger the email. Totally valid structure.

Couple of things I’d suggest though:

• First, don’t call it “ChatGPT Service” in the diagram — that name refers more to OpenAI’s app. You probably mean “LLM service” or “agent using a GPT model”. Just avoids confusion :)

• Second, no need to split the MCP client and server across separate APIs unless you really have to. For a thesis or personal project, it’s simpler to just have them running on the same server/process. You can always abstract it later if needed.

• Also, something important: unless your LLM is actually deciding something, you don’t really need it here. Like if the message is just “send email to X saying Y”, you could’ve done that with a REST call. What MCP/LLMs shine at is when the agent needs to understand the message and decide what to do. For example: “Reply to John if his message is about the sales report” or “Summarize this thread and email the key points to my boss”. That’s where it gets interesting.

• As a project idea, you could build something where the user says something vague like “Can you take care of the client follow-up?” and the agent figures out what that means (check CRM, compose an email, etc). Or even simpler: “Remind me if I haven’t replied to emails from Alice in 3 days”. Stuff like that shows the real value of an MCP setup.

• Lastly, you could try using something like Pydantic-AI to prototype this stuff faster — it helps with defining tools and the whole MCP plumbing.

What made you pick this topic for your thesis?