Provides tools for querying and analyzing BigQuery datasets across multiple Google Cloud projects, supporting operations like listing datasets, tables, analyzing columns, and executing SQL queries with security controls.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@bigquery-mcp list datasets in analytics-prod"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for secure BigQuery access across multiple Google Cloud projects.
Multi-Project Access - Query across BigQuery projects with pattern matching
Advanced Analytics - Column analysis, data quality checks, schema exploration
Security Controls - SQL validation, query limits, read-only operations
CLI Configuration - Command-line arguments with config file fallback
Docker Support - Containerized deployment for easy integration
Related MCP server: bq-readonly-mcp
Full documentation available at aicayzer.github.io/bigquery-mcp
Python 3.11+
Google Cloud SDK
Docker (optional)
gcloud auth application-default logingit clone https://github.com/aicayzer/bigquery-mcp.git
cd bigquery-mcp
pip install -r requirements.txtAdd to ~/.config/claude/claude_desktop_config.json:
{
"mcpServers": {
"bigquery": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"--volume", "/Users/YOUR_USERNAME/.config/gcloud:/home/mcpuser/.config/gcloud:ro",
"--volume", "/ABSOLUTE/PATH/TO/bigquery-mcp/logs:/app/logs",
"bigquery-mcp:latest",
"python", "src/server.py",
"--project", "your-project:*",
"--billing-project", "your-project"
]
}
}
}Add to MCP settings:
{
"bigquery": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"--volume", "/Users/YOUR_USERNAME/.config/gcloud:/home/mcpuser/.config/gcloud:ro",
"bigquery-mcp:latest",
"python", "src/server.py",
"--project", "your-project:*",
"--billing-project", "your-project"
]
}
}# Single project
python src/server.py --project "your-project:*" --billing-project "your-project"
# Multiple projects with patterns
python src/server.py \
--project "analytics-prod:user_*,session_*" \
--project "logs-prod:application_*" \
--billing-project "my-billing-project"docker build -t bigquery-mcp .
docker run -v ~/.config/gcloud:/home/mcpuser/.config/gcloud:ro bigquery-mcp \
python src/server.py --project "your-project:*" --billing-project "your-project"list_projects() - List configured BigQuery projects
list_datasets(project) - List datasets in a project
list_tables(dataset, table_type) - List tables in a dataset
analyze_table(table) - Get table structure and statistics
analyze_columns(table, columns, sample_size) - Deep column analysis
execute_query(query, format, limit, timeout) - Execute SELECT queries
python src/server.py \
--project "analytics-prod:user_*,session_*" \
--project "logs-prod:application_*" \
--billing-project "my-billing-project" \
--log-level INFO \
--timeout 20 \
--max-limit 50000bigquery:
billing_project: "your-project"
location: "EU"
projects:
- project_id: "analytics-prod"
datasets: ["user_*", "session_*"]
- project_id: "logs-prod"
datasets: ["application_*"]Fork the repository
Create a feature branch from develop
Make your changes with tests
Submit a pull request to develop
MIT License - see LICENSE file.
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