what is BigQuery MCP server?
The BigQuery MCP server is a Model Context Protocol server that provides access to Google BigQuery, allowing large language models (LLMs) to inspect database schemas and execute SQL queries.
how to use BigQuery MCP server?
To use the server, configure it with your GCP project ID and location, then execute SQL queries or list tables using the provided tools.
key features of BigQuery MCP server?
- Execute SQL queries using BigQuery dialect.
- List all tables in the BigQuery database.
- Describe the schema of specific tables.
use cases of BigQuery MCP server?
- Data analysis and reporting using SQL queries.
- Schema inspection for understanding database structure.
- Integration with LLMs for enhanced data querying capabilities.
FAQ from BigQuery MCP server?
- What is required to run the server?
You need to provide the GCP project ID and location.
- Can I specify which datasets to consider?
Yes, you can specify datasets using the
--datasetargument.
- How do I install the server?
Follow the quickstart guide to install and configure the server on your machine.
BigQuery MCP server
A Model Context Protocol server that provides access to BigQuery. This server enables LLMs to inspect database schemas and execute queries.
Components
Tools
The server implements one tool:
execute-query: Executes a SQL query using BigQuery dialectlist-tables: Lists all tables in the BigQuery databasedescribe-table: Describes the schema of a specific table
Configuration
The server can be configured with the following arguments:
--project(required): The GCP project ID.--location(required): The GCP location (e.g.europe-west9).--dataset(optional): Only take specific BigQuery datasets into consideration. Several datasets can be specified by repeating the argument (e.g.--dataset my_dataset_1 --dataset my_dataset_2). If not provided, all tables in the project will be considered.
Quickstart
Install
Claude Desktop
On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
Development/Unpublished Servers Configuration
``` "mcpServers": { "bigquery": { "command": "uv", "args": [ "--directory", "{{PATH_TO_REPO}}", "run", "mcp-server-bigquery", "--project", "{{GCP_PROJECT_ID}}", "--location", "{{GCP_LOCATION}}" ] } } ```Published Servers Configuration
``` "mcpServers": { "bigquery": { "command": "uvx", "args": [ "mcp-server-bigquery", "--project", "{{GCP_PROJECT_ID}}", "--location", "{{GCP_LOCATION}}" ] } } ```Replace {{PATH_TO_REPO}}, {{GCP_PROJECT_ID}}, and {{GCP_LOCATION}} with the appropriate values.
Development
Building and Publishing
To prepare the package for distribution:
- Sync dependencies and update lockfile:
uv sync
- Build package distributions:
uv build
This will create source and wheel distributions in the dist/ directory.
- Publish to PyPI:
uv publish
Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token:
--tokenorUV_PUBLISH_TOKEN - Or username/password:
--username/UV_PUBLISH_USERNAMEand--password/UV_PUBLISH_PASSWORD
Debugging
Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.
You can launch the MCP Inspector via npm with this command:
npx @modelcontextprotocol/inspector uv --directory {{PATH_TO_REPO}} run mcp-server-bigquery
Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
