BigQuery MCP Server

BigQuery MCP Server

The BigQuery MCP server is a specialized bridge that lets large language models (LLMs) interact directly with Google Cloud BigQuery. By exposing a set of tools that can inspect database schemas, list tables, and execute SQL queries, it eliminates the need for developers to write custom connectors or manually manage authentication flows. This capability is especially valuable when building AI assistants that must pull real‑time data, perform analytics, or generate insights from structured datasets stored in BigQuery.

At its core, the server implements three intuitive tools:

• – Retrieves a catalog of all tables within the configured BigQuery project or selected datasets, giving an LLM instant visibility into available data sources.
• – Provides the schema of a specified table, including column names, data types, and mode. This lets an assistant reason about the structure before querying.
• – Runs arbitrary SQL written in BigQuery’s dialect, returning results that the LLM can incorporate into responses or further calculations.

These tools are accessible through the Model Context Protocol, so a Claude assistant can ask the server to “list all tables in dataset ” or “execute a query that aggregates revenue by region.” The server handles authentication via the GCP project ID and location supplied at launch, optionally narrowing scope to specific datasets. This fine‑grained control ensures that only relevant data is exposed, enhancing security and reducing noise.

Developers benefit from several key advantages. First, the server abstracts away the complexity of BigQuery’s REST API and authentication mechanisms, allowing AI workflows to focus on natural‑language reasoning. Second, because the server operates over stdio and adheres strictly to MCP standards, it can be deployed locally or in CI pipelines with minimal friction. Third, the ability to describe table schemas on demand empowers LLMs to generate accurate SQL queries without prior knowledge of the database layout, enabling dynamic data exploration.

Typical use cases include building conversational analytics assistants that answer business questions on the fly, automating data‑driven reporting where an AI crafts and runs queries based on user prompts, or integrating BigQuery insights into broader multi‑tool pipelines where the LLM orchestrates data retrieval before passing results to downstream services. In each scenario, the server’s lightweight design and clear toolset make it a powerful component of modern AI‑augmented data workflows.