Google BigQuery

Run SQL queries on BigQuery datasets, list tables, and inspect schemas.

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What it can do

  • List Dataset Ids: List BigQuery dataset IDs in a Google Cloud project.
  • Get Dataset Info: Get metadata information about a BigQuery dataset.
  • List Table Ids: List table ids in a BigQuery dataset.

What data it sees

Do you need an account

No: the server works without sign-in

Run SQL queries on BigQuery datasets, list tables, and inspect schemas. Manage jobs, export results, and explore data warehouse resources.

How to connect

How to connect Google BigQuery to Claude

The server address to paste into Claude settings:

https://bigquery.run.tools

  1. Open Claude (claude.ai or the desktop app).
  2. Go to Settings → Connectors.
  3. Click Add custom connector.
  4. Paste the server address copied below into Remote MCP server URL and click Add.
  5. In a chat, click + → Connectors and switch the new connector on.

Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

Authorization

After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

How to connect Google BigQuery to ChatGPT

The server address to paste into ChatGPT settings:

https://bigquery.run.tools

  1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
  2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
  3. Open Settings → Connectors and click Create.
  4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
  5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
  6. In a new chat open + → Apps/Connectors and enable the connector.

OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

Authorization

On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

How to connect Google BigQuery to Cursor

The server address to paste into Cursor settings:

https://bigquery.run.tools

Fastest: click Open in Cursor below and confirm the prompt.

Manually:

  1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
  2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
  3. Save the file. The server appears in the MCP list; authorize it there if asked.

Authorization

If the service needs sign-in, an authorize button appears next to the server in the MCP list.

Server tool list (5)

Raw names from tools/list. Only developers need these.

list_dataset_idsList BigQuery dataset IDs in a Google Cloud project.
get_dataset_infoGet metadata information about a BigQuery dataset.
list_table_idsList table ids in a BigQuery dataset.
get_table_infoGet metadata information about a BigQuery table.
execute_sqlRun a SQL query in the project and return the result. This tool is restricted to only `SELECT` statements. `INSERT`, `UPDATE`, and `DELETE` statements and stored procedures aren't allowed. If the query doesn't include a `SELECT` statement, an error is returned. For information on creating queries, see the [GoogleSQL documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). The `execute_sql` tool can also have side effects if the the query invokes [remote functions](https://cloud.google.com/bigquery/docs/remote-functions) or [Python UDFs](https://cloud.google.com/bigquery/docs/user-defined-functions-python). All queries that are run using the `execute_sql` tool have a label that identifies the tool as the source. You can use this label to filter the queries using the label and value pair `goog-mcp-server: true`. Queries are charged to the project specified in the `project_id` field. Example Queries: -- Count the number of penguins in each island. SELECT island, COUNT(*) AS population FROM bigquery-public-data.ml_datasets.penguins GROUP BY island -- Evaluate a bigquery ML Model. SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`) -- Evaluate BigQuery ML model on custom data SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Predict using BigQuery ML model: SELECT * FROM ML.PREDICT(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Forecast data using AI.FORECAST SELECT * FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips', timestamp_col => 'date', id_cols => ['usertype'], horizon => 30)