Content
# BigQuery MCP server
[](https://smithery.ai/server/mcp-server-bigquery)
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 dialect
- `list-tables`: Lists all tables in the BigQuery database
- `describe-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](https://smithery.ai/server/mcp-server-bigquery):
```bash
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##### Development/Unpublished Servers Configuration</summary>
```json
"mcpServers": {
"bigquery": {
"command": "uv",
"args": [
"--directory",
"{{PATH_TO_REPO}}",
"run",
"mcp-server-bigquery",
"--project",
"{{GCP_PROJECT_ID}}",
"--location",
"{{GCP_LOCATION}}"
]
}
}
```
##### Published Servers Configuration
```json
"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:
1. Increase the version number in `pyproject.toml`
2. Sync dependencies and update lockfile:
```bash
uv sync
```
3. Build package distributions:
```bash
uv build
```
This will create source and wheel distributions in the `dist/` directory.
4. Publish to PyPI:
```bash
uv publish
```
Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token: `--token` or `UV_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](https://github.com/modelcontextprotocol/inspector).
You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:
```bash
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.
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