Looker MCP for AI Agents. Query Data, Audit Dashboards, Search Metadata.
The Looker MCP gives your AI agent full control over enterprise business intelligence and data analytics. Instead of clicking through dashboards or writing complex SQL, you talk to your data. This connector lets you list dashboards, execute queries against specific models, audit saved reports, and search content metadata—all via natural conversation.
Give Claude and any AI agent real-world access
List all available dashboards and search through content metadata to locate specific datasets or analytical assets.
Execute custom queries against defined models and views, fetching literal dimensions and measures on demand.
Retrieve full details about saved reports to understand the exact model mappings and filters used for consistent reporting.
Enumerate root folders and top-level models, allowing you to audit permissions and organizational structure across your entire Looker tenant.
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What AI agents can do with Looker (Business Intelligence & Data) with 7 Tools
These tools allow your agent to interact directly with the core components of your Looker instance—from listing dashboards to executing complex queries.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Looker (Business Intelligence & Data) MCPList Dashboards
Lists all available dashboards in your Looker instance.
Get Dashboard
Retrieves the complete details and query mapping for a specific dashboard ID.
List Looks
Lists all saved data mappings (Looks) that track specific datasets.
Get Look
Retrieves the full mapped details for a specified target Look object.
List Folders
Lists root folders to analyze explicit environment structures within your tenant.
Search Content
Searches content metadata across the entire instance for specific mapping targets.
Run Inline Query
Executes queries by building models to fetch literal dimensions and measures dynamically.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Looker (Business Intelligence & Data), then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Looker. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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~60% cost reduction
The BI Dashboard Graveyard Solved with Vinkius AI Gateway
Today, getting data insights means navigating a confusing web of folders, dashboards, and saved reports. You spend time opening 15 tabs just to locate the one dashboard that has the number you need, only to realize later that you can't tell if the underlying query used the correct filters or models.
With this MCP, your agent eliminates those manual steps. You simply ask it to search for 'Marketing ROI.' It uses its tools to find all relevant dashboards and reports instantly, giving you direct access to the data structure without ever touching a folder tree.
Instant Data Clarity with Looker MCP
No more clicking through dozens of menus just to verify a single metric. You can ask your agent to run an inline query and get the precise, real-time dimensions and measures you need, citing the source model.
It's about confidence. This MCP doesn't just give you data; it gives you verifiable data lineage by providing access to saved 'Looks,' letting you see exactly how every number was calculated.
What your AI can actually do with this
Getting deep insights from a large BI platform usually means getting stuck in the UI, running into permission walls, or writing complex SQL that takes forever to debug. Not anymore. This MCP connects your AI agent directly to your Looker instance, letting you manage and query data through plain English chat.
You can ask your agent to list all managed dashboards and get their detailed configuration metrics instantly. Need a specific number? You run an inline query against any model or view just by asking for the dimensions and measures. Furthermore, if you need historical context, the agent accesses saved 'Looks,' pulling out model mappings and filters so everyone sees consistent data reports.
It's like having a specialized data analyst sitting next to you, ready to answer complex questions about your organization’s metrics. Accessing this power through Vinkius makes it easy for any MCP-compatible client to connect, giving you immediate control over your entire BI environment.
019d75ca-06d9-7174-9208-13bb40850091 Here's how it actually works
The bottom line is you talk to your BI platform instead of navigating its interface.
Subscribe to this MCP and enter your specific Looker Base URL, Client ID, and Client Secret.
Connect your preferred AI client (Claude, Cursor, etc.) to the Vinkius catalog.
Ask your agent a question in natural language; it uses the tools to fetch data or metadata from Looker.
Who is this actually for?
This MCP is for anyone who spends time in complex business intelligence platforms but hates the manual effort required to navigate them. It solves the problem of being blocked by a rigid UI or having to write boilerplate SQL just to find a number.
Quickly verify dashboard configurations and run rapid inline queries using natural conversation, skipping manual SQL exports.
Search for specific reports or retrieve quick data summaries across the entire workspace without needing to know where they are stored.
Audit content metadata access and manage folder hierarchies efficiently, checking permissions across multiple Looker environments.
What Changes When You Connect
Get instant visibility into your entire BI environment. Instead of clicking through multiple menus to find out what dashboards exist, you simply ask the agent to list all available dashboards using list_dashboards.
Run complex data checks without writing SQL. You can execute queries against specific models and views just by asking for the dimensions and measures you need, leveraging run_inline_query for real-time results.
Maintain reporting consistency. If you need to know how a number was calculated months ago, you don't have to hunt through old files. The agent accesses saved 'Looks' using list_looks and provides the full mapping details.
Understand your data architecture quickly. Need to audit permissions or see what content exists? Use list_folders to enumerate root folders and understand the organizational structure across all environments.
Pinpoint exactly where information lives. When you can’t find a report, use search_content. This tool searches metadata across the instance so your agent knows where to look for that key dataset.
See it in action
Finding the Right Report After a Merger
A business user needs a report on 'Global Marketing ROI' but doesn't know which folder it lives in. They ask their agent, and it uses search_content to locate all relevant dashboards and provides the UUIDs and path for them.
Auditing Dashboard Dependencies
A platform engineer suspects a dashboard is pulling stale data. Instead of manually checking every tab, they ask their agent to use get_dashboard on the ID, instantly retrieving the detailed query structure for review.
Quick Ad-Hoc Number Check
A data analyst needs to know the total revenue from 'orders' last quarter but doesn't want to write a full SQL script. They prompt their agent to run_inline_query on the sales model and get the summary immediately.
Reviewing Historical Metrics
A manager needs proof of Q1 performance metrics. Instead of asking a colleague who built it, they ask the agent to use get_look for the saved 'Q1 Sales Overview,' confirming exactly which filters and models were applied.
The honest tradeoffs
What to watch out for, and the recommended way to handle each one.
Copying SQL into an AI chat
The user pastes a massive block of raw, complex SQL code into the agent hoping it will run it. The agent gets confused by the syntax and fails to execute anything.
Don't write full SQL. Instead, ask your agent to run_inline_query on the target model name (e.g., 'sales') and specify the fields you want. Let the tool handle the execution logic.
Searching by vague keywords
The user asks, 'Tell me about sales data.' The agent replies with a list of 50 dashboards, requiring massive manual follow-up work to find what they need.
Be specific. If you know the content area, ask the agent to search_content using known metadata names or IDs instead of general topics.
Ignoring data lineage
The user sees a number on a dashboard but can't tell if it was filtered correctly. They assume it's right and move on, risking bad decisions.
Always verify the source. Use get_look or get_dashboard to retrieve the full mapping details, confirming exactly which filters and models created that number.
When It Fits, When It Doesn't
Use this MCP if your primary bottleneck is accessing, auditing, or querying data within a mature BI platform like Looker. You need conversational control over existing dashboards, metadata, and defined data models. It's perfect for data analysts who spend time debugging ETL logic or business users who can't find the right report because they don't know the internal naming conventions.
Don't use this MCP if your goal is to build a brand new dashboard from scratch, or if you need to manually write complex, multi-step ETL processes outside of Looker. For general data retrieval that isn't structured through an existing model, consider using a direct database connector instead. This tool excels at reading and understanding what's already there.
Questions you might have
How do I find out what dashboards exist in my Looker instance using the Looker MCP? +
You use the list_dashboards tool. This instantly gives your agent a comprehensive list of every dashboard available, so you don't waste time guessing which ones are active.
Can I query data without knowing the specific SQL? Looker MCP? +
Yes. You use run_inline_query. Instead of writing code, you just tell your agent which model and which dimensions or measures you want, and it builds the query for you.
What is 'Looks' in Looker and how does the MCP help me with them? +
A 'Look' is a saved report detailing specific data mappings. The MCP lets you use list_looks and get_look to retrieve these details, ensuring consistent reporting across your organization.
If I can’t find a dashboard, what tool should I use with the Looker MCP? +
Use search_content. This searches the metadata across your entire instance. It's much better than just listing folders because it actively hunts for content based on keywords.
Does this MCP handle complex folder structures in Looker? +
Yes, you can use list_folders to enumerate the root folders and top-level models. This helps audit permissions and understand the full organizational structure of your tenant.