Starting With a Practical Use Case, Not an AI Demo
I’ve been investigating Dataverse MCP servers and where they actually fit inside real Power Platform work.
There is a lot of hype around AI agents right now. Some of it is useful. Some of it is noise. My concern is always the same: does the technology help us build, ship, and operationalize something that creates real business value?
That is where MCP servers get interesting.
The goal here is not to make an agent that sounds impressive. The goal is to give it practical access to the systems and structure it needs to do real work. In this case, that means working directly against Dataverse and a Power Platform solution.
For this run-through, I used a simple HR scenario: automated user provisioning. The setup starts with a schema JSON file that defines a table called Kumo AD Groups, and then uses Claude Code connected to a Dataverse MCP server to provision that table.
What’s Actually Happening in the Demo
Let’s walk through what the agent is doing, because this is where it becomes real.
First, I give a plain instruction inside Claude Code: provision the Kumo AD Group table using the schema JSON file.
From there, the MCP server gives the agent awareness of the environment. It can see the solution context, understand the schema, and begin assembling the steps needed to create the table.
The agent then starts generating a script to execute that work. In this case, it builds out a Python-based approach to interact with Dataverse and apply the schema.
There is a pause, a retry, and then a prompt asking how to proceed because the initial solution reference was incorrect. I select the correct existing solution, and the process continues.
That detail matters. This is not a one-click automation. It is guided execution, where the agent can do meaningful work but still relies on clear input and validation.
Where the Value Starts to Show
I think the real value is not that a table gets created. We have always been able to do that.
The value is that the agent understands enough about the Power Platform environment to participate in the execution process. It can interpret a schema, generate the steps, and move the work forward.
That starts to move AI out of the ideation layer and into actual delivery.
For teams working in Power Platform, Dataverse, and Copilot Studio, that shift matters. Instead of treating AI as something separate from your systems, you begin embedding it into how those systems are built and maintained.
What to Watch For
The demo is intentionally not perfect.
After the table is created, it does not immediately appear in the expected view. I have to go into the solution, add an existing table, and search for it. Once I do, I can confirm that it was created correctly, including the proper publisher prefix.
Candidly, that is the part most demos skip. But it is also the part that tells you whether something is usable in a real environment.
- Did it land in the right place?
- Did it follow naming conventions?
- Is it aligned with your governance model?
Those are the questions that determine whether this scales.
Where This Goes Next
At the end of the day, Dataverse MCP servers are interesting because they give AI agents a more practical role inside the Microsoft stack.
Not as a replacement for good architecture. Not as a shortcut around governance. But as a way to simplify repetitive setup, accelerate early-stage build work, and help teams move from idea to implementation faster.
For use cases like HR provisioning, internal tooling, and structured Dataverse development, this is a pattern worth paying attention to.
If you want to see the full process, including where it works and where it needs intervention, it is much clearer in the video.
If you want to see me work through this in real time, watch the full walkthrough here:
If your team is exploring how to operationalize AI agents inside Power Platform, Kumo can help frame the roadmap, validate the right use cases, and build the governance model around it.
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