Google BigQuery MCP for AI agents

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

CALL ANY TOOL

List datasets and tables, inspect schemas, run SQL queries with user-level IAM, and monitor jobs.

bigquery_batch_delete_row_access_policies

Batch delete row access policies

Delete multiple row access policies from a BigQuery table in a single call.

Parameters

Name

Type

Required

Description

dataset_id

string

Required

The ID of the dataset containing the table

policy_ids

array

Required

The IDs of the row access policies to delete

project_id

string

Required

The Google Cloud project ID that owns this BigQuery resource.

table_id

string

Required

The ID of the table to delete row access policies from

force

boolean

Optional

If true, allows removing all row access policies on the table even though this would make the table fully accessible to all existing table readers

bigquery_cancel_job

Cancel job

bigquery_delete_dataset

Delete dataset

bigquery_delete_model

Delete model

bigquery_delete_row_access_policy

Delete row access policy

bigquery_get_dataset

Get dataset

bigquery_get_job

Get job

bigquery_get_query_results

Get query results

bigquery_get_routine

Get routine

bigquery_get_row_access_policy

Get row access policy

bigquery_get_row_access_policy_iam_policy

Get row access policy iam policy

bigquery_get_table

Get table

bigquery_insert_dataset

Insert dataset

bigquery_insert_job

Insert job

bigquery_insert_row_access_policy

Insert row access policy

bigquery_insert_table

Insert table

bigquery_list_datasets

List datasets

bigquery_list_jobs

List jobs

bigquery_list_projects

List projects

bigquery_list_routines

List routines

bigquery_list_table_data

List table data

bigquery_replace_dataset

Replace dataset

bigquery_replace_table

Replace table

bigquery_run_query

Query run

bigquery_set_routine_iam_policy

Set routine iam policy

bigquery_test_row_access_policy_iam_permissions

Test row access policy iam permissions

bigquery_test_table_iam_permissions

Test table iam permissions

bigquery_update_dataset

Update dataset

bigquery_update_model

Update model

bigquery_update_row_access_policy

Update row access policy

For more tools, view docs.

Build your Agent

Drop the toolkit in, point it at the user, and your analytics agent can use Google BigQuery 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: ["googlebigquery"], toolNames: ["gbq_datasets_list", "gbq_tables_list", "gbq_table_schema"] },
  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: ["googlebigquery"], toolNames: ["gbq_datasets_list", "gbq_tables_list", "gbq_table_schema"] },
  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: ["googlebigquery"], toolNames: ["gbq_datasets_list", "gbq_tables_list", "gbq_table_schema"] },
  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/googlebigquery",
    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 agent to start using Google BigQuery.

Schema & discovery

Copy the prompt

Copied

List all datasets in [project].

Copy the prompt

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Show the schema of [dataset.table].

Copy the prompt

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Which tables are in [dataset]?

Copy the prompt

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What views exist in [dataset]?

Query & analysis

Copy the prompt

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Run: SELECT DATE(event_time), COUNT(*) FROM events GROUP BY 1.

Copy the prompt

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Top 10 customers by revenue this quarter.

Copy the prompt

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DAU by tier for the last 30 days.

Copy the prompt

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Churn cohort analysis for Q3.

Monitoring & cost

Copy the prompt

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Which queries scanned the most data today?

Copy the prompt

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Job status for [job_id].

Copy the prompt

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Estimated cost for query: [SQL].

Copy the prompt

Copied

Slowest queries in the last 6 hours.

SEE HOW AUTH WORKS

Users authorize Google BigQuery once. Their credentials stay vaulted, every call is checked, and every action is logged.

Authorize

Your user connects

Google BigQuery

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 BigQuery

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 BigQuery

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 BigQuery

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 agents and MCP connectors. Working code, live demos, fork what fits.

GTM and RevOps Teams

Revenue forecast commentary

Pulls open pipeline from Salesforce and HubSpot, calculates coverage against quota, flags at-risk stages, posts commentary to Slack, and logs every snapshot to Google Sheets.

GTM and RevOps Teams

Deal intelligence agent

Pulls recent Gong calls, scores deal risk with an LLM, cross-references the record in Attio, and DMs each owner their at-risk deals in Slack. Every read is scoped to that rep's own access.

Engineering Teams

Engineering standup agent

Pulls commits from GitHub and GitLab, tracks issue movement in Jira, and posts a per-engineer standup brief to Slack. Each engineer's activity is read on their own delegated OAuth.

GTM and RevOps Teams

Competitive intelligence briefing agent

Scans Gong calls for competitor mentions, matches each one to its Notion battlecard, and DMs every affected rep a single Slack digest per cycle. Every call runs as the PMM who owns the briefing, never a shared bot.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

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 BigQuery 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 Google BigQuery as the user or as a shared key?

As the user. Each workspace member authorizes once and Scalekit resolves their credential at request time. Audit logs attribute every action to that user, not a shared service account.

Where is the Google BigQuery oauth 2.0 stored?

In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Tokens never appear in prompts, logs, or LLM context.

Can I limit what the agent is allowed to do in Google BigQuery?

Yes. Pass a tool name filter to listScopedTools so the analytics agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Google BigQuery.

What happens when a user revokes Google BigQuery access?

The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.

Does the agent inherit the user's BigQuery IAM roles?

Yes. Every query runs with the authorizing user's permissions. IAM roles, column-level security, and row-level access policies all apply. The agent cannot query data the user's account cannot reach.

On this page

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