BigQuery MCP Server

The Ergut BigQuery MCP Server bridges the gap between conversational AI assistants and Google Cloud’s analytical engine, enabling developers to query massive datasets without writing SQL or exposing raw credentials. By adopting the Model Context Protocol, this server acts as a secure intermediary that translates natural‑language prompts into validated BigQuery queries, executes them, and returns results in a format that Claude (or any MCP‑compatible model) can ingest directly. This eliminates the friction of manual query construction and streamlines data exploration workflows.

At its core, the server provides a read‑only gateway to BigQuery resources. When an LLM receives a question such as “What were our top 10 customers last month?”, the MCP server parses the intent, formulates an appropriate SELECT statement, and runs it against the specified project and location. Results are returned with clear schema annotations—tables versus materialized views—so the assistant can contextualize the data before presenting it. The server enforces a default 1 GB query limit to protect against runaway costs, while still allowing developers to adjust limits if needed.

Key capabilities include:

• Natural‑language query translation that abstracts SQL complexity.
• Schema discovery: the assistant can list datasets, tables, and views with metadata for better user guidance.
• Secure access via Google Cloud IAM; the server itself operates with read‑only permissions, ensuring that sensitive data remains protected.
• Resource filtering: developers can expose only selected datasets or restrict access to specific views, tailoring the assistant’s knowledge base.
• Integration with Claude Desktop: a single configuration entry in launches the server, making it effortless to add BigQuery support to an existing AI workflow.

Real‑world scenarios benefit from this tight coupling: data analysts can ask a question in plain English and receive instant answers; product managers can explore usage metrics without leaving the chat; developers can prototype dashboards by iterating on natural‑language prompts. The MCP server’s design also supports future extensions—adding authentication layers, custom query templates, or cost‑tracking hooks—without changing the LLM interface. In sum, the Ergut BigQuery MCP Server turns raw analytical data into conversational knowledge, empowering teams to make faster, data‑driven decisions with minimal technical overhead.