Built a production RAG chatbot with custom MCP servers as the action layer, sharing what I learned

I've been building agentic tooling at work and wanted to share one pattern that worked. Instead of a chatbot that only retrieves and answers, I wired custom MCP servers in as the action layer, so staff trigger live workflows (create records, pull reports, start processes) from natural language.

A few takeaways:

• Separating retrieval (RAG over docs) from actions (MCP tools) made the system far easier to debug
• Most of the real work was edge cases in how the model decides when to act vs answer
• Clear tool descriptions mattered more than prompt tuning

Happy to go deeper in comments. I'm a full-stack engineer, in SF May 26 to June 10 looking for my next role in AI/agents, so if your team works on this, feel free to reach out.