Awesome
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
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:
--token
orUV_PUBLISH_TOKEN
- Or username/password:
--username
/UV_PUBLISH_USERNAME
and--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.