BIGQUERY_README.md•3.99 kB
# BigQuery MCP Server
The BigQuery Model Context Protocol (MCP) Server enables AI-powered development tools to seamlessly connect, interact, and generate data insights with your BigQuery datasets and data using natural language commands.
## Features
An editor configured to use the BigQuery MCP server can use its AI capabilities to help you:
- **Natural Language to Data Analytics:** Easily find required BigQuery tables and ask analytical questions in plain English.
- **Seamless Workflow:** Stay within your CLI, eliminating the need to constantly switch to the GCP console for generating analytical insights.
- **Run Advanced Analytics:** Generate forecasts and perform contribution analysis using built-in advanced tools.
## Prerequisites
* [Node.js](https://nodejs.org/) installed.
* A Google Cloud project with the **BigQuery API** enabled.
* Ensure [Application Default Credentials](https://cloud.google.com/docs/authentication/gcloud) are available in your environment.
* IAM Permissions:
* BigQuery User (`roles/bigquery.user`)
## Install & Configuration
1. In the Antigravity MCP Store, click the "Install" button.
2. Add the required inputs in the configuration pop-up, then click "Save". You can update this configuration at any time in the "Configure" tab.
> [!NOTE]
> If you encounter issues with Windows Defender blocking the execution, you may need to configure an allowlist. See [Configure exclusions for Microsoft Defender Antivirus](https://learn.microsoft.com/en-us/microsoft-365/security/defender-endpoint/configure-exclusions-microsoft-defender-antivirus?view=o365-worldwide) for more details.
You'll now be able to see all enabled tools in the "Tools" tab.
### Usage
Once configured, the MCP server will automatically provide BigQuery capabilities to your AI assistant. You can:
* **Find Data:**
* "Find tables related to PyPi downloads"
* "Find tables related to Google analytics data in the dataset bigquery-public-data"
* **Generate Analytics and Insights:**
* "Using bigquery-public-data.pypi.file_downloads show me the top 10 downloaded pypi packages this month."
* "Using bigquery-public-data.pypi.file_downloads can you forecast downloads for the last four months of 2025 for package urllib3?"
## Server Capabilities
The BigQuery MCP server provides the following tools:
| Tool Name | Description |
|:-----------------------|:----------------------------------------------------------------|
| `execute_sql` | Executes a SQL query. |
| `forecast` | Forecast time series data. |
| `get_dataset_info` | Get dataset metadata. |
| `get_table_info` | Get table metadata. |
| `list_dataset_ids` | Lists dataset IDs in the database. |
| `list_table_ids` | Lists table IDs in the database. |
| `analyze_contribution` | Perform contribution analysis, also called key driver analysis. |
| `search_catalog` | Search for tables based on the provided query. |
## Custom MCP Server Configuration
The BigQuery MCP server is configured using environment variables.
```bash
export BIGQUERY_PROJECT="<your-gcp-project-id>"
export BIGQUERY_LOCATION="<your-dataset-location>" # Optional
export BIGQUERY_USE_CLIENT_OAUTH="true" # Optional
```
Add the following configuration to your MCP client (e.g., `settings.json` for Gemini CLI, `mcp_config.json` for Antigravity):
```json
{
"mcpServers": {
"bigquery": {
"command": "npx",
"args": ["-y", "@toolbox-sdk/server", "--prebuilt", "bigquery", "--stdio"]
}
}
}
```
## Documentation
For more information, visit the [BigQuery documentation](https://cloud.google.com/bigquery/docs).