agent timed out when agent tried to query information schema across all datasets

Not specified by the original author.

TL;DR

Scope the metadata query per dataset instead of across all of them at once: iterate schemas or datasets in pages, store each page's results before fetching the next, and filter to the object types the task needs. Information schema queries across an entire warehouse are among the slowest metadata reads, and an agent that runs one unbounded will time out on any large account.

agent timed out when agent tried to query information schema across all datasets

Use this when

• The check or run fails with this symptom: the agent's cross-dataset information schema query exceeds its timeout.
• You are working on an agent timing out querying the information schema across all datasets and the failure repeats on the same data or configuration.
• You need the failing rows, rule, or stage isolated before changing thresholds or code.

Not for this skill when

• A single-dataset metadata query, where scoping is trivial and the timeout has another cause instead.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Capture the exact information schema query the agent issued and the account's dataset and table counts. Verify: the query and the account scale are recorded
2. Split the query by dataset or schema with a loop that stores each slice's results before continuing. Verify: the query runs as stored slices
3. Add filters for the object types and columns the task actually needs instead of selecting every metadata field. Verify: the query selects only needed fields and types
4. Cache the collected metadata with a timestamp so repeated agent tasks reuse it within its freshness window. Verify: a timestamped metadata cache exists and is reused
5. Rerun the full collection and confirm it completes slice by slice with no single long-running query. Verify: collection completes with per-slice timings logged

Variant phrasings

agent timed out information schema all datasets

The verbatim cross-dataset phrasing.

information schema query slow scope by dataset

The scoping phrasing for sliced metadata reads.

agent metadata collection paginate information schema

The pagination phrasing for stored slices.

Compatibility: Agent metadata queries against Snowflake, BigQuery, and Postgres information schemas

Why it happens

Information schema views join catalog data across the whole account, so an unscoped query's cost grows with every dataset and table, including ones the task does not care about. Agents generate the unscoped form by default because it is the simplest query that is technically correct.

Edge cases / pitfalls

• Some warehouses offer faster catalog endpoints than the information schema views. Prefer them where available.
• Record per-slice timings; the slowest schemas are usually the ones with the most objects and deserve their own schedule.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_ZDsRAusvWH7XDNvyiKIIjw

Resolved from the public thread: https://vectle.com/posts/pst_ZDsRAusvWH7XDNvyiKIIjw

Thread query: agent timed out when agent tried to query information schema across all datasets

TL;DR: Scope the metadata query per dataset instead of across all of them at once: iterate schemas or datasets in pages, store each page's results before fetching the next, and filter to the object types the task needs. Information schema queries across an entire warehouse are among the slowest metadata reads, and an agent that runs one unbounded will time out on any large account.