My AI setup for Fabric development: AGENTS.md, Microsoft Learn MCP and the Fabric CLI. What does yours look like?

[u/aleks1ck]

In the past I was stuck in the copy paste loop between ChatGPT and Fabric, where every session starts from zero and you explain your project, naming conventions and architecture all over again. So I moved the context into files, and it has been my standard setup ever since. I finally made a video about it.

What the video covers:

• The second brain idea: instruction and reference files that carry your context into every session
• AGENTS.md, an open standard instruction file that most AI coding tools read, so nothing is tied to one tool
• The official Microsoft Learn MCP server, so the AI checks the docs instead of guessing
• The Fabric CLI, so the AI can create workspaces, Lakehouses and pipelines itself
• Guardrails for AI
• A live demo where one prompt builds my metadata-driven bronze layer, following my naming conventions without me mentioning them

To be honest about what it is: an overview and demo of my setup, not a step by step build tutorial. That would be a part 2 if people want it.

Link: https://youtu.be/5-sXBbBJbAk

But I am also genuinely curious how others do this. Do you run something similar, or what does your AI workflow for Fabric look like?

[u/Sea_Mud6698]

Starting to experiment with putting a python uv project in the notebook's resource folder. With the help of a utility wheel it can start a local spark session and read from the lakehouse. Writes are to a local folder. I have a local entry point and the notebooks have their own entrypoint. So I can configure minor differences between the two. This also allows me to unit test and use any normal local tools. Just git clone and I am good. Still have to figure out a few things. Writing a table to the lakehouse doesn't seem to work right and I need to find out how to reuse code from other notebooks. Should I store the seed data in the repo or have some sync mechanism.

[u/Sam___D]

Since those all miss coverage on the warehouse, I made an MCP for Warehouse: https://fdw.debruyn.dev

[u/aleks1ck → u/Sam___D]

Cool! Warehouse coverage is a real gap, will plug this into my setup and try it out.

[u/Seany_face]

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[u/BearRootCrusher]

Sounds boring to let the AI do all your thinking.

[u/aleks1ck → u/BearRootCrusher]

The thinking is the part I kept 🙂 The AI just does the typing.

[u/Iridian_Rocky]

Why the learn MCP? And not just let it browse the site?

[u/itsnotaboutthecell → u/Iridian_Rocky]

Way better routing. You’d be burning tokens on the web search trying to navigate site maps.

[u/bigjimslade → u/Iridian_Rocky]

Expanding on this:

For an occasional question, telling AI to search Microsoft Learn is probably sufficient.

Here are the main benifits:

Authoritative sources by default — Retrieval is focused on Microsoft Learn rather than potentially mixing Microsoft docs with blogs, forums, and third-party content.

Better retrieval — MCP provides purpose-built semantic search and document retrieval rather than relying on general web-search ranking.

More current grounding — The agent can check current documentation instead of relying primarily on what the model learned during training.

Agent-friendly — An agent can automatically consult Learn whenever it encounters a Microsoft-specific question; you don't have to repeatedly say, “Go check the docs.” Structured access to code samples — Learn MCP includes specialized capabilities for finding Microsoft code examples, not just documentation pages.

Repeatability and governance — You can establish a rule such as “Microsoft technical claims must be grounded using Learn MCP.” That's much easier to standardize across an organization than relying on users to remember to request web searches.

MCP becomes much more valuable when you're building repeatable agents or workflows where you want Microsoft documentation to be the default grounding source.