BigQuery Data Platform

Report when tables were last written, to catch stale or dead sources. Several plausible-looking tables on this platform stopped being updated without being dropped, so a query against one silently returns old data. Check before trusting a table you have not used before. Free — reads table metadata only, scanning no data. Args: dataset_id: The dataset to check, e.g. "events_raw". table_id: A single table to check. Omit to report every table in the dataset, which is the faster way to spot a dead one. environment: Which configured BigQuery environment to use. Omit to use the default. Call list_environments to see what exists.

Find which Colab notebooks and saved queries reference a table. The question to ask before changing or dropping a table: ``list_scheduled_queries`` says what writes it, this says who reads it. Unlike the other tools here this one opens every asset it considers, which costs Dataform read quota. It is bounded by ``max_assets`` and reports how much of the project it actually covered -- a result is evidence about the assets scanned, never proof that nothing else uses the table. Args: table: Table name to search for. A bare name matches any qualification; 'dataset.table' or a fully-qualified name narrows it. environment: Which configured environment to read. Omit for the default. asset_type: Restrict to 'sql', 'notebook' or 'data_canvas'. max_assets: Ceiling on how many bodies to read.

Return one Colab notebook or saved query's contents, by name or id. Notebook outputs are stripped -- across 52 real notebooks they were 77% of the bytes, and none of the logic. Args: asset: Display name (as shown in BigQuery Studio) or the asset id. environment: Which configured environment to read. Omit for the default.

Get one scheduled query in full: its SQL, destination, and recent runs. Call this after list_scheduled_queries to see why a query is failing, or what SQL actually produces a table. Args: query: The scheduled query's name, or the id from list_scheduled_queries. runs: How many recent runs to include, newest first. environment: Which configured environment to look in.

Get a table's columns, partitioning, size and freshness. Free — scans no data. Call this before writing a query, for two reasons beyond column names: - `partitioning` says whether a WHERE clause can actually limit the scan. A date-shaped column name does NOT mean the table is partitioned; if this field is null, every query reads the whole table. - Nested columns are expanded to dotted paths and flagged `repeated`, which is what tells you a column needs UNNEST. Args: dataset_id: The dataset, e.g. "events_raw". table_id: The table or view name. environment: Which configured BigQuery environment to use. Omit to use the default. Call list_environments to see what exists.

List Colab notebooks and saved queries in BigQuery Studio. Use this for anything the user calls a Colab notebook, Colab Enterprise notebook, "colab script", BigQuery notebook, saved query or data canvas -- BigQuery Studio stores all of them as code assets and this lists them all. Free -- this reads metadata only and never opens an asset. Bodies are what cost quota, so filter here first and open individual assets afterwards. Args: environment: Which configured environment to read. Omit for the default. asset_type: Restrict to one of 'sql', 'notebook', 'data_canvas'. Saved queries usually outnumber notebooks by a wide margin, so this is the difference between a readable answer and 600 rows. name_contains: Case-insensitive substring match on the display name. limit: Maximum assets to return.

List the BigQuery datasets available in the data platform project. Call this first to discover what data exists. Free — scans no data. Args: environment: Which configured BigQuery environment to use. Omit to use the default. Call list_environments to see what exists.

List the configured BigQuery environments and which one is the default. Call this when the user names an environment you have not seen, or when a question could plausibly be about more than one. Free — reads only this server's configuration.

List scheduled queries: what they write, when they run, and their state. Use this to answer "what populates this table?" and "why is this table stale?" — a disabled or failing scheduled query is the usual cause, and check_table_freshness can see the staleness but not the reason. The SQL is not included here; call get_scheduled_query for one of them. Args: dataset: Only queries writing into this destination dataset. contains: Only queries whose name contains this text. include_disabled: Keep disabled queries in the result. They are the most likely explanation for a table that stopped updating, so this defaults to True. environment: Which configured environment to look in. Scheduled queries are regional, so this must be the environment whose location holds them.

List tables and views inside a dataset. Free — scans no data. Args: dataset_id: The dataset to inspect, e.g. "events_raw". environment: Which configured BigQuery environment to use. Omit to use the default. Call list_environments to see what exists.

Run a read-only (SELECT/WITH) SQL query against BigQuery and return rows. Cost safety: the query is ALWAYS dry-run first to estimate how much data it will scan. If that estimate is above the warning threshold, the query does NOT run — instead this returns `status: "confirmation_required"` with the estimated size and cost. Stop there, tell the user the estimated scan and cost, and ask. Only re-call with confirm_expensive=true once they have agreed: that flag records the user's decision, not yours. Queries above the hard cap never run, even with confirmation. Always fully-qualify tables as `<project>.<dataset>.<table>`, and check get_table_schema first — a WHERE clause only limits the scan on a table that is actually partitioned. Args: sql: A SELECT (or WITH ... SELECT) query. max_rows: Max rows to return, to keep responses small. 0 (the default) uses the server's configured limit. confirm_expensive: Set True only after the user has agreed to a query previously flagged as costly. Leave False for the first attempt. environment: Which configured BigQuery environment to query. Omit to use the default. Call list_environments to see what exists.