mcp-server-bigquery

# 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Support/Claude/claude_desktop_config.json` On Windows: `%APPDATA%/Claude/claude_desktop_config.json` ##### Development/Unpublished Servers Configuration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.