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AI + loop + MCP tools → from a question to an answer

  • April 22, 2026
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dmitribagh
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I spent a few hours trying to understand MCP and, like many things, it didn’t really click until I started building.

First, I played with MCPCaller using tools created by others. That part was interesting, but not very insightful. Those tools were already answering questions directly, so I didn’t really see the need for anything more.

It only clicked when I pointed AI at my own data.

I built a few simple MCP tools (workspaces) in FME (discover, profile, values, filter…), and instead of wiring a fixed workflow, I let an AI planner decide what to do next. That’s where the loop comes in:

“Let’s see what’s here → what’s relevant → get values → compare → accumulate knowledge → answer.”

That’s when MCP started to make sense.

MCP tools are not about AI. It’s about exposing tools in a way that allows AI to use them efficiently.

The important part is not MCPCaller itself, but the pattern:
AI + loop + MCP tools → from a question to an answer.

I recorded a short demo with a mix of datasets (SQLite, DWG, Revit, Excel) — different questions, but just one workspace It even fits ​@donatsafe’s self-imposed “under 10 transformers” rule 😄

Now I’m curious:
What do you think about this pattern?
And what MCP tools would you build?

Dmitri

 

      5 replies

      donatsafe
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      • donatsafeSafer
      • Safer
      • April 22, 2026

      I love it Dmitri.  An AIPlanner transformer and you keep iterating until done.  

      hkingsbury
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      • hkingsburyCelebrity
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      • April 22, 2026

      Interesting thanks ​@dmitribagh

      I’m in a similar situation to you, where I’m still trying to understand exactly how MCP can be utilised. In your example:

      • What role(s) do the MCP transformers perform? What/where are they calling?
      • What is the AIPlanner transformer doing?
      • For the last example, how does this approach in FME differ from passing that same file to another AI agent?
      User15904968838663764003
      Contributor
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      Indeed, this opens a lot of doors. Finally, the custom transformer loop is out in the main tab. This will make it much easier to explain/understand data pipelines compared to using custom transformers. Thanks to the team who implemented this!

      dmitribagh
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      • dmitribaghSafer
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      • April 23, 2026

      Interesting thanks ​@dmitribagh

      I’m in a similar situation to you, where I’m still trying to understand exactly how MCP can be utilised. In your example:

      • What role(s) do the MCP transformers perform? What/where are they calling?
      • What is the AIPlanner transformer doing?
      • For the last example, how does this approach in FME differ from passing that same file to another AI agent?

      Hi ​@hkingsbury 

      1. The first MCPCaller just asks for available tools through my MCP server. This way, the AI planner knows what it can use throughout his investigation. W actually plan to make a simple improvement, which will eliminate the need in JSONTemplater here.
        The second MCPCaller uses one the tools with a query, both are defined by the AIPlanner.
         
      2. AIPlanner is just a simple custom transformer that uses AI (can be anything), and its goal is to pass user question, available tools and responses from MCP to itself each new iteration. This way, it accumulates knowledge and once it has enough to answer the question, it stops iterating and outputs the final answer. 
      3. The last example works exactly like all others. AIPlanner checks the question, checks available tools and then answers the question using only those tools. If there are not tools to answer a particular question, AI will give a reason why it wasn’t able to complete the task. The beauty of MCP is that an author of MCP tools may decide their MCP server needs another tool or lots of new tools, and that would mean that this workspace will simply become smarter and will be able to answer next time, we don’t need to change anything in this workspace.

      I hope this answers your questions

      Dmitri

        hkingsbury
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        • hkingsburyCelebrity
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        • April 23, 2026

        Thanks Dmitri, that’s really good insight. I think I need to do something similar to you and dive in and go have an explore!

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