Lucashild MCP Server Bigquery

An MCP server that provides access to BigQuery databases, supporting functions such as query execution and table structure viewing

Database Developer tools #Database Query #BigQuery #MCP Protocol #Data Architecture .Python

rating : 2 points

downloads : 8

update time : 2025-04-29

What is BigQuery MCP Server?

The BigQuery MCP Server is a bridge between language models and Google's BigQuery data warehouse. It allows AI models to explore database structures and retrieve data through SQL queries without needing direct database access.

How to use BigQuery MCP Server?

The server runs as a background service that your AI application can communicate with. It provides three main functions: listing available tables, showing table structures, and running SQL queries.

Use Cases

Ideal for AI applications that need to analyze business data, generate reports, or answer data-driven questions by querying large datasets in BigQuery.

Key Features

Query ExecutionRun SQL queries in BigQuery dialect and get results in a format that AI models can understand.

Schema ExplorationDiscover available tables and their structures to help construct proper queries.

Dataset FilteringOptionally limit access to specific datasets for better security and performance.

Pros and Cons

Advantages

Secure way for AI to access BigQuery data without direct credentials

Simplifies complex database interactions for language models

Supports filtering datasets for focused access

Limitations

Requires Google Cloud Platform setup

Limited to BigQuery's SQL dialect features

Needs proper configuration for optimal performance

Getting Started

Install the Server

Configure your Claude application to run the MCP server by editing the configuration file.

Set Up Credentials

Ensure you have proper GCP credentials with BigQuery access.

Configure Server

Specify your GCP project and location when starting the server.

Start Querying

Your AI application can now use the server to explore and query data.

Example Use Cases

Sales Data AnalysisAn AI assistant queries monthly sales figures to generate a performance report.

Customer SegmentationThe AI explores customer data tables to identify different customer segments.

Frequently Asked Questions

What permissions does the server need in GCP?

Can I limit which tables are accessible?

How do I debug query issues?

Additional Resources

Google BigQuery Documentation

Official BigQuery documentation and guides

MCP Inspector GitHub

Tool for debugging MCP server interactions

Google Cloud IAM Guide

Information on setting up proper permissions

🚀 BigQuery MCP Server

A Model Context Protocol server that provides access to BigQuery. This server enables LLMs to examine database schemas and execute queries.

🚀 Quick Start

📦 Installation

Claude Desktop

On MacOS: ~/Library/Application\ 支持/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json%

Development/Unreleased Server Configuration

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

Released Server 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 appropriate values.

✨ Features

Tools

The server implements the following tools:

execute-query: Execute SQL queries using the BigQuery dialect.
list-tables: List all tables in the BigQuery database.
describe-table: Describe the schema of a specific table.

📚 Documentation

Configuration

The server can be configured with the following parameters:

Property	Details
`--project` (Required)	GCP project ID.
`--location` (Required)	GCP location (e.g., `europe-west9`).
`--dataset` (Optional)	Consider only specific BigQuery datasets. Multiple datasets can be specified by repeating the parameter (e.g., `--dataset my_dataset_1 --dataset my_dataset_2`). If not provided, all tables in the project will be considered by default.

🔧 Technical Details

Build and Release

To prepare the distribution package:

Synchronize dependencies and update the lock file:

uv sync

Build the package distribution:

uv build

This will create source and wheel distributions in the dist/ directory. 3. Publish to PyPI:

uv publish

Note: You 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 the MCP server runs via stdio, debugging can be difficult. For the best debugging experience, we highly recommend using MCP Inspector.

You can start the MCP Inspector by executing the following command via npm:

npx @modelcontextprotocol/inspector uv --directory {{PATH_TO_REPO}} run mcp-server-bigquery

After starting, the Inspector will display a URL that you can access in your browser to start debugging.

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