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BigQuery: AI-Driven Schema Exploration & Scalable Analytics - MCP Implementation

BigQuery: AI-Driven Schema Exploration & Scalable Analytics

Empower LLMs to seamlessly explore database schemas and run BigQuery analytics at scale—bridge the gap between AI and enterprise data with effortless query execution." )

Research And Data
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Users create an average of 34 projects per month with this tool

About BigQuery

What is BigQuery: AI-Driven Schema Exploration & Scalable Analytics?

Imagine a Swiss Army knife for data enthusiasts—this server acts as a quirky sidekick for AI models, letting them snoop around BigQuery databases like a digital detective. It’s not just another tool; it’s the gateway to schema snooping and query magic, all while keeping your data governance on lock.

How to use BigQuery: AI-Driven Schema Exploration & Scalable Analytics?

Start by configuring your mission control with mandatory project and location parameters. If you’re feeling choosy, specify datasets like a picky diner selecting entrees. Once your server’s humming, let AI models loose to list tables, dissect schemas, or fire off SQL queries. Oh, and don’t forget the “--dataset” repeat trick for those indecisive moments.

BigQuery Features

Key Features of BigQuery: AI-Driven Schema Exploration & Scalable Analytics?

  • Tool Trio: The “execute-query” lets AI flex its SQL muscles, while “describe-table” and “list-tables” act as data’s Wikipedia for curious models.
  • Configurability: Geolocation specificity meets dataset granularity—because one-size-fits-all is for socks, not databases.
  • Debugging Delight: The MCP Inspector is your crash-test dummy for server troubleshooting, complete with a browser-based control panel.

Use Cases of BigQuery: AI-Driven Schema Exploration & Scalable Analytics?

BigQuery FAQ

FAQ from BigQuery: AI-Driven Schema Exploration & Scalable Analytics?

  • “Do I need to be a GCP wizard?” Just know your project ID and location—no dragon slaying required.
  • “Can I debug with style?” Absolutely. The MCP Inspector’s browser view is basically debugging with a side of TikTok aesthetics.
  • “What if my queries get too chatty?” The “--dataset” filter is your mute button for noisy datasets.
  • “Publishing to PyPI—a total nightmare?” Nope, just sync, build, and publish. The hardest part is remembering your token password.

Content

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 with the following arguments:

  • --project (required): The GCP project ID.
  • --location (required): The GCP location (e.g. europe-west9).
  • --dataset (optional): 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). If not provided, all tables in the project will be considered.

Quickstart

Install

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. Sync dependencies and update lockfile:

bash uv sync

  1. Build package distributions:

bash uv build

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

  1. 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.

You can launch the MCP Inspector via 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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