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Query, analyze, and compare Google BigQuery data using natural language.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "bigquery-mcp" yet — see the docs or source repo.
Inspect the schema of the orders table in BigQuery project my-project, dataset sales. List field names, types, nullability, and briefly explain each field's likely business meaning in English.
A schema listing with field descriptions to help quickly understand the table.
Using the BigQuery dataset sales, write and run SQL to calculate daily order count, revenue, and average order value for the last 30 days, sorted by date ascending, then summarize the main trends.
Executable SQL, query results, and a brief analysis of trend changes.
Compare the schema and record volume differences between dataset_a.customers and dataset_b.customers in BigQuery. Identify added, missing, or type-changed fields and describe possible migration risks.
A comparison report covering schema changes, size differences, and migration risk notes.
Query and explore BigQuery databases with natural language and schema discovery.
Analyze BigQuery search and analytics data with conclusions and actionable recommendations.
Connect to BigQuery so AI can inspect schemas and run queries.
Connect LLMs to Google BigQuery for querying and data analysis tasks.
Query BigQuery in natural language with schema exploration and history tracking.
Query and explore PhysioNet biomedical datasets and schemas through BigQuery.