Google Looker connector for AI agents

Tools your agent reaches for on Google Looker, scoped per user.

CALL ANY TOOL

Browse dashboards, run Looks, query LookML models, and access BI data. Same toolkit, every framework, no auth plumbing.

googlelooker_get_dashboard

Get Dashboard

Retrieve the full metadata of a Looker dashboard by its ID, including all tile definitions (charts, tables, text, filters), layout, linked Looks, and underlying queries.

Parameters

Name

Type

Required

Description

dashboard_id

string

Required

The ID of the Looker dashboard to retrieve

fields

string

Optional

Comma-separated list of dashboard fields to include in the response

googlelooker_get_look_results

Get Look Results

googlelooker_list_dashboards

List Dashboards

googlelooker_list_explores

List Explores

googlelooker_list_folders

List Folders

googlelooker_list_looks

List Looks

googlelooker_list_models

List Models

googlelooker_run_inline_query

Query run inline

googlelooker_run_look

Run Look

Build your Agent

Drop the toolkit in, point it at the user, and your agent can query Looker dashboards and run Looks from the first run.

import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { z } from "zod";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["googlelooker"], toolNames: ["looker_dashboards_list", "looker_look_run", "looker_query_run"] },
  pageSize: 100,
});

const lcTools = tools.map((t) => new DynamicStructuredTool({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  schema: z.object({}).passthrough(),
  func: async (args) => {
    const { data } = await sk.tools.executeTool({
      toolName: t.tool.definition.name,
      identifier: "user_123",
      params: args,
    });
    return JSON.stringify(data);
  },
}));

const agent = createReactAgent({ llm, tools: lcTools });

import { ScalekitClient } from "@scalekit-sdk/node";
import OpenAI from "openai";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["googlelooker"], toolNames: ["looker_dashboards_list", "looker_look_run", "looker_query_run"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  type: "function",
  function: {
    name: t.tool.definition.name,
    description: t.tool.definition.description,
    parameters: t.tool.definition.input_schema,
  },
}));

const resp = await openai.responses.create({
  model: "gpt-4o", input: prompt, tools: llmTools,
});

import { ScalekitClient } from "@scalekit-sdk/node";
import Anthropic from "@anthropic-ai/sdk";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["googlelooker"], toolNames: ["looker_dashboards_list", "looker_look_run", "looker_query_run"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  input_schema: t.tool.definition.input_schema,
}));

const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-6", max_tokens: 1024,
  tools: llmTools,
  messages: [{ role: "user", content: prompt }],
});

import { Agent } from "@google/adk/agents";
import {
  MCPToolset, StreamableHTTPConnectionParams,
} from "@google/adk/tools/mcp";

const toolset = new MCPToolset({
  connectionParams: new StreamableHTTPConnectionParams({
    url: "https://mcp.scalekit.com/googlelooker",
    headers: { Authorization: `Bearer ${userScopedToken}` },
  }),
});

const agent = new Agent({
  name: "agent", model: "gemini-2.0-flash",
  tools: await toolset.getTools(),
});

Try these prompts

Paste any prompt into your analytics agent to start querying Looker BI data.

Search & recall

Copy the prompt

Copied

List all dashboards in the [folder name] folder.

Copy the prompt

Copied

Run the [Look name] Look and return the results.

Copy the prompt

Copied

Search for dashboards related to [topic].

Query & analyze

Copy the prompt

Copied

Query the [model].[explore] Explore for [fields] filtered by [condition].

Copy the prompt

Copied

Get all available dimensions in the [explore] explore.

Copy the prompt

Copied

Run the [Look ID] Look and return the top [N] rows.

SEE HOW AUTH WORKS

Analysts authorize Looker once. Their Google credentials stay vaulted, every query runs under their Looker permissions, and every access is logged.

Authorize

Your user connects

Google Looker

once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access

Who:

user ‘A’

when:

Once per user

access:

Limited to user

Store

Their

Google Looker

token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection

vault:

encrypted

scope:

per-user

tokens:

auto-refreshed

Resolve

When your agent calls a

Google Looker

tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs

speed:

~40ms

check:

before every call

seen by:

nobody

Audit

Every

Google Looker

tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it

history:

90 days

export:

SIEM-ready

logged:

every call

Test other agents

Same per-user auth pattern across other analytics and BI connectors.

SALES

Deal intelligence agent

Combine Gong, Attio, and Slack signals to surface deal risks and next-best actions. Updated after every call.

ENGINEERING

Engineering standup agent

Aggregate GitHub and GitLab activity, link to Jira, and post a daily standup digest to Slack. No async updates.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought. Scalekit starts with user identity, scope enforcement, and audit.

01.

Shared tokens break per-user analytics

A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential so attribution, audit, and scope stay accurate.

// shared token
 audit → bot_service_account
 user_filter → broken

 // scalekit
 audit → user_abc
 scope → enforced ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Google Looker today. Others tomorrow.

“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”

Venu Madhav Kattagoni

Head of Engineering / Von

FAQs

Frequently Asked Questions

Does the agent access Looker as the user or a shared service account?

As the user. Each analyst authorizes once and Scalekit resolves their credential. Queries run with that analyst's Looker row-level security and field access.

Where is the Looker OAuth token stored?

In Scalekit's AES-256 vault, namespaced per tenant. Tokens never appear in prompts or LLM context.

Can I restrict the agent to specific models or Explores?

Yes. Looker's own user-attribute-based access controls apply per user. Scalekit enforces credentials and audits calls on top.

What happens when an analyst revokes Looker access?

The connection is invalidated on the next tool call. Subsequent requests fail closed. Other analysts remain unaffected.

Does this work with self-hosted Looker Core?

Yes. Set the proxy_url to your Looker Core instance endpoint. The auth pattern and vault behavior are identical.

On this page

Tool and actions Framework Compatibility Starter Prompt Auth Lifecycle Try Other Agents Why Scalekit