What is BigQuery MCP Server?

A Model Context Protocol server that enables LLMs to access Google BigQuery, inspect database schemas, and execute queries.

How to Use BigQuery MCP Server?

1. Installation: Use Smithery for automatic installation or set up manually via Claude Desktop configuration.
2. Configuration: Provide GCP project ID, location, and optionally dataset(s) and service account key.
3. Execution: Use provided tools to query databases or inspect schemas.
4. Debugging: Utilize MCP Inspector for stdio-based debugging.

Key Features

• Schema Inspection: Tools like list-tables and describe-table for database structure analysis.
• Query Execution: execute-query tool for running SQL queries using BigQuery dialect.
• Flexible Configuration: Supports command-line arguments and environment variables.
• Security Options: Can use default credentials or service account key files.
• Dataset Filtering: Option to limit to specific datasets.

Use Cases

1. Data Analysis: Execute complex SQL queries against BigQuery datasets.
2. Schema Understanding: Inspect database structures before querying.
3. LLM Integration: Enable language models to interact with BigQuery for data retrieval.
4. Custom Data Operations: Build applications that require dynamic BigQuery access.

FAQ

• **What are the required configuration parameters? Required: GCP project ID and location. Optional: datasets and key file.
• **How do I install the server? Prefer Smithery for automatic installation or configure manually in Claude Desktop.
• **Can I use this without a service account key? Yes, default credentials will be used if no key file is provided.
• **How do I debug this server? Use the MCP Inspector accessible via npm command.

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 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:

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

"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:

1. Increase the version number in pyproject.toml

2. Sync dependencies and update lockfile:

uv sync

3. Build package distributions:

uv build

This will create source and wheel distributions in the dist/ directory.

4. Publish to PyPI:

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.

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.