BigQuery MCP
Give AI agents governed, read-safe SQL access to your BigQuery warehouse
A Model Context Protocol integration connects autonomous AI agents to BigQuery so they can inspect schemas, compose and run SQL, and reason over results within guardrails you set. Agents answer analytical questions, monitor metrics, and generate reports directly against the warehouse without a human writing every query. We scope access to specific datasets, enforce dry-run cost checks, and keep write paths deliberate.
Agents list datasets, tables, and columns and read descriptions to ground queries in the real data model before writing SQL.
Agents execute parameterized SELECT queries with dry-run byte estimates and row limits so answers stay accurate and cost-bounded.
Agents interpret returned rows, compute deltas against prior periods, and produce plain-language findings with the SQL that backs them.
With explicit permission, agents materialize results into scratch tables or scheduled marts for downstream dashboards and activation.
We deploy an agent that turns questions like 'why did blended ROAS drop last week' into schema-aware SQL, runs it against BigQuery, and returns a narrated answer with the query and caveats attached.
An agent queries key metric tables on a schedule, compares against baselines, and raises a diagnosed alert — segment, channel, and probable cause — when spend, conversion, or revenue drifts.
Agents draft, dry-run, and validate the SQL behind new marts, then hand tested queries to the team so recurring reports get built in hours instead of sprints.
Not by default. We scope the MCP connection to read-only roles on named datasets, block DDL and DML unless explicitly granted, and route any write to reviewable scratch destinations. Least-privilege service accounts enforce the boundary at the IAM layer.
Every agent query goes through a dry run that returns the bytes it would scan, and we enforce maximum-bytes-billed limits and row caps. Queries above threshold are rejected or require confirmation before execution.
The MCP server exposes dataset, table, and column metadata plus descriptions, so the agent inspects the real model first. We enrich key tables with column documentation, which measurably improves the SQL agents generate.
Let's grow.
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