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BigQuery

In one line: Connect Google BigQuery so your UnleashX agent can run SQL queries, browse datasets and tables, and pull analytics results — all in plain language.

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1. Overview

BigQuery is Google Cloud’s serverless data warehouse. It stores your analytics data in datasets and tables, and runs fast SQL queries across huge volumes without any infrastructure to manage. Once connected to UnleashX, your agent can run SQL against your BigQuery project, list datasets and tables, describe a table’s schema, preview rows, and return query results. It works against your live project using your Google sign-in, so it acts with your own IAM permissions. Connecting BigQuery to UnleashX turns your warehouse into something you can ask questions of in plain language. Instead of opening the BigQuery console to write a query, you tell your agent what you want to know and it runs the SQL and reports the answer.

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2. What you’ll need

  • A Google Cloud project with BigQuery enabled, and its project ID.
  • IAM permission to run queries and read the datasets you want the agent to access (typically BigQuery Data Viewer + BigQuery Job User, or BigQuery User).
  • A Google account (or service account) with access to that project.
  • An UnleashX account with access to Data Connectors.
No API key to paste. BigQuery uses Google OAuth — you click Authorize and sign in with Google. You’ll also provide your project ID.
BigQuery bills for bytes scanned by queries. A broad SELECT * over a large table can be expensive. Have the agent select only the columns and date ranges it needs, and consider setting a maximum bytes billed limit in your project.

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3. Get your credentials

BigQuery MCP authenticates with a Google OAuth 2.0 access token that has access to your project. The token is sent to Google’s APIs as a Bearer credential.
1

Open your Google Cloud project

Go to the Google Cloud Console and select the project whose data you want to connect. Note its Project ID — you’ll provide this to your agent.
2

Confirm the API is enabled

Under APIs & Services → Library, make sure the BigQuery API is enabled for the project.
3

Check your IAM roles

In IAM & Admin, confirm your account has at least BigQuery Data Viewer (read tables) and BigQuery Job User (run queries) on the project — or BigQuery User.
4

Authorize through UnleashX

When connecting (Section 4), sign in with Google and grant the scopes below. UnleashX uses the resulting token as a Bearer credential and refreshes it automatically.Scopes UnleashX requests for BigQuery:
Prefer read-only access? Grant https://www.googleapis.com/auth/bigquery.readonly (or the BigQuery Data Viewer + Job User roles without write access) so the agent can query and browse but not modify data.

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4. Connect on UnleashX

1

Open your agent

Go to https://www.tryunleashx.com and open the agent you want to connect.
2

Open Data Connectors

Inside the agent, go to Data Connectors.
3

Find BigQuery

Locate BigQuery and click Connect (or Add / Configure).
4

Authorize with Google

Click Authorize, sign in with Google, and grant the scopes. Enter your Project ID when prompted.
5

Confirm success

You return to UnleashX and the BigQuery connector shows a Connected badge.

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Use BigQuery in a Workflow

Once connected, you can add BigQuery to any automation from the Workflows builder. Its triggers and tools appear in the Apps panel, marked with an MCP badge.
1

Add a trigger or action node

Open Workflows → New Workflow. On the canvas, click + Add Trigger (or the + below any node) to add a step.Add a trigger on the workflow canvas
2

Pick BigQuery from the Apps panel

In the Paths panel, open the Apps tab and select BigQuery. Use the search box if you have many connectors.Choose your app from the Apps list
3

Choose a trigger or tool

Pick the action you want (for example, “run query”). Configure its fields — required fields are marked with a red asterisk (*).
4

Add or select your account

Under Selected account, choose an already-connected account, or click Add Account to connect one now.
5

Save and test

Fill in the remaining fields and click Save. Use Test to verify the step, then toggle Publish when the workflow is ready.
The steps are the same for every connector. For the full workflow builder guide, see Using MCP in Workflows.

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5. Available tools

*Read-only SELECT queries don’t change data. If you run DML (INSERT/UPDATE/DELETE/MERGE) or DDL, Run Query can modify data — connect with read-only scope/roles to keep the agent limited to queries.

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6. Example usage

“What was our total revenue by month this year? Query analytics.sales.” → Runs Run Query with a SELECT … GROUP BY month against analytics.sales and returns the monthly totals. “List the tables in the events dataset and show me the schema of page_views.” → Runs List Tables on events, then Get Table on page_views to return its columns and types.

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7. Permissions & data access

UnleashX can:
  • Run SQL queries and fetch their results.
  • Dry-run queries to estimate cost before running them.
  • List and describe datasets and tables, and preview rows.
  • List and inspect query jobs — all subject to your IAM permissions.
UnleashX cannot:
  • Access datasets or tables your account isn’t granted in IAM.
  • Query a project other than the one you configured.
  • Manage IAM, billing, or project-level Google Cloud settings.
To disconnect: In UnleashX, open Data Connectors → BigQuery → Disconnect. You can also revoke access at https://myaccount.google.com/permissions (Google account → Third-party access). Disconnecting revokes access immediately.

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8. Troubleshooting

For general MCP issues, see /mcp/integrations.

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9. Frequently asked questions

Is my data stored by UnleashX? No. UnleashX runs queries and reads metadata live through the BigQuery API per request; it doesn’t copy your tables. Will queries cost money? BigQuery bills by bytes scanned. Use Dry Run Query to estimate cost first, and select only the columns and date ranges you need. Can the agent modify data? Only if your account has write access and you run DML/DDL. Connect with read-only scope or roles for a query-only agent. Can I use a service account instead? Yes — for automation, a service account with least-privilege BigQuery roles is often preferable to an individual Google login.

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10. References