Mcp Server Bigquery
What is Mcp Server Bigquery
mcp-server-bigquery 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.
Use cases
Use cases for mcp-server-bigquery include data analysis, reporting, and integrating LLMs with BigQuery for enhanced data-driven applications.
How to use
To use mcp-server-bigquery, install it via Smithery using the command ‘npx -y @smithery/cli install mcp-server-bigquery --client claude’. Configure the server with required arguments such as project ID and location, and then run it to start querying BigQuery.
Key features
Key features include executing SQL queries with BigQuery dialect, listing all tables in the database, and describing the schema of specific tables.
Where to use
undefined
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Mcp Server Bigquery
mcp-server-bigquery 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.
Use cases
Use cases for mcp-server-bigquery include data analysis, reporting, and integrating LLMs with BigQuery for enhanced data-driven applications.
How to use
To use mcp-server-bigquery, install it via Smithery using the command ‘npx -y @smithery/cli install mcp-server-bigquery --client claude’. Configure the server with required arguments such as project ID and location, and then run it to start querying BigQuery.
Key features
Key features include executing SQL queries with BigQuery dialect, listing all tables in the database, and describing the schema of specific tables.
Where to use
undefined
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
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 either with command line arguments or environment variables.
| Argument | Environment Variable | Required | Description |
|---|---|---|---|
--project |
BIGQUERY_PROJECT |
Yes | The GCP project ID. |
--location |
BIGQUERY_LOCATION |
Yes | The GCP location (e.g. europe-west9). |
--dataset |
BIGQUERY_DATASETS |
No | 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) or by joining them with a comma in the environment variable (e.g. BIGQUERY_DATASETS=my_dataset_1,my_dataset_2). If not provided, all datasets in the project will be considered. |
--key-file |
BIGQUERY_KEY_FILE |
No | Path to a service account key file for BigQuery. If not provided, the server will use the default credentials. |
Quickstart
Install
Installing via Smithery
To install BigQuery Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install mcp-server-bigquery --client claude
Claude Desktop
On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
Development/Unpublished Servers Configuration
Published Servers Configuration
Replace {{PATH_TO_REPO}}, {{GCP_PROJECT_ID}}, and {{GCP_LOCATION}} with the appropriate values.
Development
Building and Publishing
To prepare the package for distribution:
-
Increase the version number in
pyproject.toml -
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.
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
