Google Big Query

Server Details

The BigQuery remote MCP server is a fully managed service that uses the Model Context Protocol to connect AI applications and LLMs to BigQuery data sources. It provides secure, standardized tools for AI agents to list datasets and tables, retrieve schemas, generate and execute SQL queries through natural language, and analyze data—enabling direct access to enterprise analytics data without requiring manual SQL coding.

Status: Healthy
Last Tested: 2026-03-23 18:30
Transport: Streamable HTTP
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Available Tools

5 tools

execute_sqlInspect

Run 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.

The execute_sql tool can also have side effects if the the query invokes remote functions or Python UDFs.

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)

ParametersJSON Schema

Name	Required	Description
`query`	Yes	Required. The query to execute in the form of a GoogleSQL query.
`dryRun`	No	Optional. If set to true, BigQuery doesn't run the job. Instead, if the query is valid, BigQuery returns statistics about the job such as how many bytes would be processed. If the query is invalid, an error returns. The default value is false.
`projectId`	Yes	Required. Project that will be used for query execution and billing.

get_dataset_infoInspect

Get metadata information about a BigQuery dataset.

ParametersJSON Schema

Name	Required	Description	Default
`datasetId`	Yes	Required. Dataset ID of the dataset request.
`projectId`	Yes	Required. Project ID of the dataset request.

get_table_infoInspect

Get metadata information about a BigQuery table.

ParametersJSON Schema

Name	Required	Description
`tableId`	Yes	Required. Table ID of the table request.
`datasetId`	Yes	Required. Dataset ID of the table request.
`projectId`	Yes	Required. Project ID of the table request.

list_dataset_idsInspect

List BigQuery dataset IDs in a Google Cloud project.

ParametersJSON Schema

Name	Required	Description	Default
`projectId`	Yes	Required. Project ID of the dataset request.

list_table_idsInspect

List table ids in a BigQuery dataset.

ParametersJSON Schema

Name	Required	Description	Default
`datasetId`	Yes	Required. Dataset ID of the table request.
`projectId`	Yes	Required. Project ID of the table request.

Verify Ownership

Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:

{
  "$schema": "https://glama.ai/mcp/schemas/connector.json",
  "maintainers": [
    {
      "email": "your-email@example.com"
    }
  ]
}

The email address must match the email associated with your Glama account. Once verified, the connector will appear as claimed by you.

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