BI and data governance · Available
Looker for Claude, ChatGPT and Copilot
Questions typed into Claude, ChatGPT or Copilot can be answered by Looker itself, from its LookML models. The assistant picks an Explore, chooses dimensions and measures already defined there, and returns the result with a link to the Explore. Access filters and user attributes keep each person to their rows, and every question is logged. The guided pilot for Looker starts with the Explores your developers trust most.
- Status
- Available
- Setup
- Guided pilot with your team
- AI assistants
- Claude, ChatGPT, Copilot Studio
- Access
- By role, view only or with approvals
Ask in a sentence
What can your team ask Looker?
Questions from marketing manager, finance analyst, looker developer and others, answered from live Looker data and only for the people allowed to see them.
Records it works with
- LookML models
- Explores
- Looks
- Dashboards
- Schedules
- Folders
- User attributes
- Marketing manager
“From the Marketing Attribution Explore, which channels drove the most pipeline last quarter?”
- Finance analyst
“Using the Revenue Explore, what is net revenue retention by customer segment for the past year?”
- Looker developer
“Which dashboards in the Shared folder show errors in the content validator?”
- Account executive
“Which of my accounts in the Product Usage Explore dropped more than 25% in weekly active users?”
- Head of operations
“What does the Operations KPIs dashboard show for on-time delivery by carrier this month?”
- Analytics lead
“Which Explores were queried most last month, and by which teams?”
Changes, governed
What the assistant can prepare in Looker
The assistant never saves on its own. It prepares the change, the person checks the preview and confirms, and anything above your limits waits for an approver.
- 1 Prepare The assistant drafts the change from the request.
- 2 Preview and confirm The person sees exactly what will change.
- 3 Approve Above your limits, a second person decides.
- 4 Saved and logged Written to Looker under a named person.
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Schedule a Look to deliver to the sales team every Monday
Internal deliveries go ahead once the person agrees; outside addresses are blocked unless the Looker admin approves.
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Save an Explore result as a Look in a team folder
A personal folder needs nothing more; a shared folder needs its owner, since others build on what is saved there.
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Set an alert on a dashboard tile for a threshold
Alerts that would notify more than 20 people need the analytics lead, to keep noise down across teams.
Example rules. During the pilot you set the limits, the approvers and which roles may prepare each change.
Why it needs care
Governance built for Looker
Looker exists so that revenue and churn mean one thing across the company. Assistants go through the Explores each role may use and never around the LookML model, so access filters and user attributes still apply. A schedule or saved Look appears as a preview before it is created. Each query keeps the name of the person who asked.
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Sign-in by work email
Only people you invite, with a one-time code. No shared logins.
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Roles per tool
Each role reaches only the Looker records it needs.
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View only, if you prefer
Admins can keep a connection to lookups with one switch.
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A preview first
Every change is shown in full before anything is saved.
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Approval rules
Above your limits, a second person approves the change.
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One audit log
Every request tied to a named person, ready for review.
Setup
How Looker gets connected
We connect Looker for your team in a guided pilot, on the same gateway every app uses. No code on your side.
- 01
Agree the questions
We list the Looker questions and changes your team needs, and who may use each.
- 02
Connect with your admin
Your Looker administrator creates a dedicated integration user with only that access.
- 03
Test with your people
A small group tries it in Claude, ChatGPT or Copilot while we tune the tools.
- 04
Roll out with rules
Roles, approval limits and the audit log are in place before everyone joins.
Then people use it from the assistant they already have
Looker questions
Does the assistant write SQL against our warehouse?
No. It asks Looker, choosing an Explore and the fields defined in your LookML model. Looker then generates the SQL, as it does for people, so joins and metric definitions stay exactly as your developers wrote them.
Can it use Looker user attributes for row limits?
Yes. Queries run as a Looker user mapped to the person, so access filters based on user attributes such as region or brand apply. During the pilot we confirm the mapping for each role with real people.
Does it work with Looker Studio?
This connection is for Looker, the LookML platform. Looker Studio reports are a separate product, and many of them read from BigQuery, which mcpbizapps can connect to as well in its own pilot.
Does mcpbizapps keep a copy of our Looker data?
No. When someone asks a question, mcpbizapps reads the answer live from Looker through a dedicated integration user and returns it without storing it. The audit log records who asked, which tool ran and the result, with personal fields and free text removed.
Looker is a trademark of its owner. mcpbizapps is not affiliated with Google; the name describes the connection only.
Related
Often connected alongside Looker
Part of Data and analytics: Cloud warehouses, dashboards, governance and data pipelines.
Start
Bring Looker into your AI assistant
Tell us how your team uses Looker, and we will plan a guided pilot around your own questions.
- We agree the tools and approval rules with you
- Your admin creates the integration user; no code
- Business data is read live and not stored
Thank you
We received your request and will reply by email to find a time.
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