BigQuery

What is mcp-server-bigquery?

mcp-server-bigquery is a Model Context Protocol (MCP) server written in Python that provides large language models (LLMs) with access to Google BigQuery. It allows LLMs to inspect database schemas and execute SQL queries on BigQuery.

How to use mcp-server-bigquery?

Installation

Via Smithery:

npx -y @smithery/cli install mcp-server-bigquery --client claude

Manual Configuration for Claude Desktop: For development/unpublished servers, add the following to your Claude Desktop configuration file (e.g., ~/Library/Application Support/Claude/claude_desktop_config.json on MacOS):

"mcpServers": {
  "bigquery": {
    "command": "uv",
    "args": [
      "--directory",
      "{{PATH_TO_REPO}}",
      "run",
      "mcp-server-bigquery",
      "--project",
      "{{GCP_PROJECT_ID}}",
      "--location",
      "{{GCP_LOCATION}}"
    ]
  }
}

For published servers:

"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 your specific values.

Configuration

The server can be configured using 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.

Key Features of mcp-server-bigquery

• BigQuery Integration: Seamlessly connects LLMs to Google BigQuery databases.
• Schema Inspection: Allows LLMs to inspect database schemas, understanding table structures and relationships.
• SQL Query Execution: Enables LLMs to execute SQL queries directly on BigQuery.
• Tools Provided:
  • 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.
• Flexible Configuration: Supports configuration via command-line arguments and environment variables.

Use Cases of mcp-server-bigquery

• Natural Language Database Interaction: Empowering users to query BigQuery databases using natural language through LLMs.
• Automated Data Analysis: Automating data retrieval and analysis tasks by allowing LLMs to interact with BigQuery.
• LLM-powered Data Exploration: Enabling LLMs to explore and understand BigQuery datasets without manual intervention.
• Building Intelligent Data Applications: Developing applications where LLMs can dynamically access and manipulate data in BigQuery.

FAQ

Q: What is the Model Context Protocol (MCP)? A: The Model Context Protocol is a standard that allows LLMs to interact with external tools and services, providing them with context and capabilities beyond their core language generation.

Q: What are the required configuration parameters? A: The --project (or BIGQUERY_PROJECT) and --location (or BIGQUERY_LOCATION) parameters are required to specify your GCP project ID and location, respectively.

Q: Can I limit the BigQuery datasets the server considers? A: Yes, you can use the --dataset argument or BIGQUERY_DATASETS environment variable to specify particular datasets. If not provided, all datasets in the project will be considered.

Q: How can I debug the mcp-server-bigquery? A: It is recommended to use the MCP Inspector for debugging. You can launch it with npx @modelcontextprotocol/inspector uv --directory {{PATH_TO_REPO}} run mcp-server-bigquery.

Q: How can I install mcp-server-bigquery? A: You can install it via Smithery using npx -y @smithery/cli install mcp-server-bigquery --client claude or configure it manually for Claude Desktop.

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.