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BI MCP or Warehouse MCP? A 7-toggle rubric for picking the right doorway

One question I keep getting as a BI MCP builder serving a large enterprise user base: which MCP should I use for this use case? Not "which one is better." Which doorway for this job. The question usually arrives in a s

One question I keep getting as a BI MCP builder serving a large enterprise user base: which MCP should I use for this use case?

Not "which one is better." Which doorway for this job.

The question usually arrives in a stronger form: "Why do we need a BI MCP when the warehouse MCP does the job?" The lazy answer is "because BI has a semantic layer." That's true and useless. It doesn't tell a team which doorway to give their agent on Monday.

I got tired of answering it from scratch every time, so I turned the answer into seven toggles. Here they are β€” and there's an interactive version at the end that gives you the verdict.

The 7 questions

1. Who's asking?
Others β€” stakeholders, customers, anyone non-technical β†’ BI MCP. They can't validate raw SQL; they need answers built from vetted definitions.
You, and you know the data β†’ warehouse MCP. A data engineer asking for themselves can read the SQL, spot a bad join, re-ask. No curation needed.

2. Does it need a defined business metric?
Yes β€” "revenue," "churn," "active user" β†’ BI MCP. If two people can define the term differently, the definition must come from the model, not the model's imagination.
No β€” row counts, null rates, "what's in this table" β†’ warehouse MCP. No business definition at stake; raw SQL is the honest tool.

3. One-off or repeated?
Repeated β€” dashboards, weekly reports β†’ BI MCP. Repeated questions deserve a curated path: same definition, same shape, every time.
One-off β€” debugging, "let me check something" β†’ warehouse MCP. Ad-hoc work dies in curation queues.

4. Do the consumers have warehouse access?
No β†’ BI MCP, and this one is decisive. It's the only doorway that works. The BI tool queries under its own service identity while the end user has zero warehouse access; authorization happens at the BI project level. This is not a security hole β€” it's how BI works. The semantic layer is the authorization boundary.
Yes β†’ either. Through the warehouse MCP the agent acts with the requester's own grants.

5. Exploring or asking a known question?
Exploring a new table, "what's possible here" β†’ warehouse MCP. The curated model can't answer questions it wasn't built for.
Known business question β†’ BI MCP.

6. Does the agent need to write or create?
Yes β€” DDL, staging tables, "build me a mart" β†’ warehouse MCP. Decisive. The BI doorway is read-only by design.
No β†’ either.

7. Does the semantic model cover this?
No β†’ warehouse MCP, or go model it first. The honest caveat the semantic-layer pitch skips: BI MCP is only as good as the model behind it. An unmodeled question through the BI doorway gets a confident wrong answer or no answer.

The decision rule

Two of these are decisive: no warehouse access β†’ BI MCP; needs writes β†’ warehouse MCP. Otherwise, majority wins. A tie means hybrid β€” explore in the warehouse MCP, operationalize the metric in the semantic layer, serve it through BI MCP.

"Warehouse MCP" covers BigQuery, Snowflake, Databricks, Fabric. "BI MCP" covers Looker, dbt, Cube β€” any semantic-layer doorway.

Try it

I built the rubric as an interactive page β€” seven toggles, live verdict:
https://sangaraju1988.github.io/mcp-doorway-rubric/

And the repo (CC BY 4.0 β€” use it, credit it):
https://github.com/sangaraju1988/mcp-doorway-rubric

Two caveats

Managed warehouse MCP endpoints ship with guardrails β€” row caps, runtime caps. They rarely bind for aggregated Q&A, but bulk extraction belongs to the warehouse API, not any MCP doorway.

And unguarded agent + raw warehouse = surprise bills. Cost guardrails (quotas, dry runs, billed-bytes limits) belong in the warehouse MCP setup.

Most mature teams end up running both doorways. The rubric just tells you which one for this job.

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Originally published by Dev.to AI. Aggregated on AIWithGhost for educational purposes β€” full credit and traffic to the original publisher.