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Improvado's MCP server connects your Looker instance to AI agents. Ask business questions against your existing LookML models, run explores, and pull dashboard data — without opening Looker every time. Works with Claude, Cursor, and any MCP-compatible tool.
Your LookML models already define your business logic. Now your AI agent can use them. Ask for revenue by segment, funnel conversion by cohort, or retention by product line — and the MCP server translates it into Looker API calls against your existing explores.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
Create and update scheduled reports, configure dashboard filters, and manage Looker content programmatically. The operational work that normally means navigating deeply nested menus — done in one conversation.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
Set AI-powered alerts on the Looker metrics that matter. Track when KPIs breach thresholds, when dashboard data goes stale, or when explore results change significantly week-over-week.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
Create and update scheduled reports, configure dashboard filters, and manage Looker content programmatically. The operational work that normally means navigating deeply nested menus — done in one conversation.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
Everyone knows Looker exists, but only a handful of analysts know how to navigate explores and write custom looks. Every ad-hoc business question lands in a Slack DM to an analyst. Response time is hours. During planning cycles, analysts are buried.
Improvado's MCP server exposes your existing LookML models to AI agents. Non-technical stakeholders ask questions in plain English — the AI maps them to the correct explore and dimensions. Analysts focus on modeling, not answering repetitive lookups.
You want to correlate marketing spend from one explore with revenue outcomes from another. Looker doesn't let you join across explores natively. The workaround is downloading two CSVs, opening Excel, and joining them manually. By the time you're done, the numbers are stale.
Your AI agent queries multiple Looker explores in sequence and synthesizes the results. It pulls ad spend from your marketing explore and LTV from your revenue explore, then builds the correlation in the same conversation — no CSV exports needed.
Your Looker instance has 400 dashboards built over three years. Half are duplicates or stale. When someone asks 'what's our churn rate,' three different dashboards show three different numbers because they use different date logic and segment filters. Nobody knows which to trust.
Ask your AI agent to identify which Looker dashboards cover a given topic, when they were last updated, and how their underlying explores differ. Surface the authoritative source without digging through 400 dashboards manually.
Same MCP connection, different workflows for every team member. Agency CEOs get portfolio health. Media Strategists get campaign QA. Analysts get cross-platform reports. Account Managers get auto-generated QBR decks. Creative Directors get performance-based briefs.
Each role asks in natural language. The MCP server handles the complexity — rate limits, auth, schema normalization, governance — behind the scenes.
Yes. The MCP server connects to your Looker instance and uses your existing explores, dimensions, and measures. You don't need to redefine your data model — the AI queries what's already there. Any explore accessible via the Looker API is queryable through the MCP connection.
No. That's the point. You ask in plain English and the MCP server translates your question into Looker API calls against the right explore. Your LookML models define the business logic; the AI figures out how to query them.
Both. You can pull data from specific explores for ad-hoc analysis, or you can query existing dashboards and tiles directly. You can also list and manage dashboards, scheduled reports, and content.
The MCP server authenticates with your Looker API credentials. It inherits the exact same data access controls as the account you connect — row-level access, model permissions, and folder visibility are all respected. The AI only sees what that account can see.
The Looker MCP integration supports both approaches. It can retrieve results from saved Looks and dashboards, and it can also execute ad-hoc queries against your LookML models using Looker's Run Inline Query API. This means an AI agent can translate a natural-language question into the appropriate LookML explore, dimensions, measures, and filters, then execute the query in real time. Organizations benefit from having governed, centrally defined LookML metrics as the trusted source rather than raw SQL.
The Looker MCP integration operates under the permissions of the Looker API credentials used to configure it. If the integration uses a service account, that account's Looker model and data access permissions apply to all queries, including any user attribute-based row-level security rules defined in your LookML models. For organizations requiring per-user data isolation, it is recommended to configure the integration with appropriately scoped credentials or to consult Looker's embed and API authentication documentation to understand how user context can be passed through.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
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