BigQuery MCP Integration

BigQuery is Google Cloud's fully managed data warehouse, designed for running fast SQL queries on large datasets. The BigQuery MCP integration with Weldable lets your AI agents query your data warehouse, retrieve results, and generate reports through natural language, turning your agent into an on-demand analytics assistant.

Your agent translates plain English questions into SQL, executes them against your BigQuery datasets, and returns formatted results. No need to write queries by hand or wait for someone else to pull the data.

Use cases

Answer business questions with live data

Your AI agent can write and execute SQL queries against your BigQuery datasets based on plain-language questions. Ask about revenue trends, user signups by region, or product usage patterns, and the agent returns results directly from your data warehouse. This is especially valuable for non-technical stakeholders who need data but do not write SQL.

Build automated reporting pipelines

Set up your agent to run specific BigQuery queries on a schedule and deliver results to Slack, Google Sheets, or email. Weekly revenue reports, daily active user counts, or monthly churn metrics can all be generated and distributed without human involvement. This replaces manual report generation and ensures numbers are always current.

Validate data quality

Your agent can run data quality checks against your BigQuery tables, looking for null values in required columns, duplicate records, or unexpected data distributions. This helps data teams catch issues early before they affect downstream dashboards and models. Schedule these checks to run daily for continuous data validation.

How it works

Connect BigQuery to Weldable using a Google Cloud service account with BigQuery access. Once configured on the integrations page, your AI agent translates natural language questions into SQL queries, executes them against your datasets, and returns formatted results. The agent understands your schema and can reference specific tables and columns. Weldable handles authentication and query execution automatically.

Tips

Share your schema context. Tell your agent which datasets and tables are relevant to your questions. The more schema context it has, the more accurate its queries will be. Mention table names and key column names when asking questions.

Set query cost limits. BigQuery charges by data scanned. Consider configuring maximum bytes billed on your queries to prevent expensive accidental full-table scans. This is especially important when giving your agent access to large tables.

Start with read-only access. Grant the service account only bigquery.dataViewer and bigquery.jobUser roles initially. You can expand permissions later if your workflows require writing data or managing datasets.