agent context overflow during pull Metabase query results: context limit

Not specified by the original author.

TL;DR

Shrink what the agent carries. Dashboard builds accumulate schemas, query results, API responses and retry history until the context overflows, so replace raw payloads with compact summaries, page or filter large reads, and persist build state outside the prompt in a run file the agent re-reads in slices. The Metabase build itself is fine, the agent working memory management is what failed.

agent context overflow during pull Metabase query results: context limit error failed the run

Use this when

• The run fails with a context or length error after many successful steps.
• Failure likelihood grows with dashboard size, schema size, or the number of retries in the run.
• Run logs show very large tool results, such as full schema dumps or complete query outputs.

Not for this skill when

• The run fails on its first step with a single huge input file. Split that input directly rather than redesigning run state.
• The model in use has a known small limit and the task is modest. Switching to a larger context model may be the whole fix.

Steps

1. Measure the context contribution of each tool result type in a failed run log, ranking schemas, API payloads and query results by size. Verify: the largest contributors to the overflow are named
2. Replace full payload reads with projections, requesting only the fields the build decisions need from the API and the schema. Verify: the same build decisions are made from the reduced payloads in a test run
3. Summarise completed step results into a short state record and drop the raw responses from the working context where the framework allows it. Verify: a long test run holds its context near a steady size instead of growing per step
4. Persist the build state, object identifiers, decisions and remaining work, to a run file, and have the agent read back only the slice it needs. Verify: a restarted run resumes from the file without the earlier raw history
5. Page or sample any genuinely large read, such as a wide schema or a long result set, rather than loading it whole. Verify: the build handles the largest target dashboard in the test set
6. Rerun the original Metabase build end to end and confirm it completes within the context budget. Verify: the run finishes and the dashboard renders correctly

Why it happens

Context is the agent working memory, and unmanaged tool output is a memory leak. Frameworks default to keeping everything because dropping information feels risky, but the cost is a hard failure late in the longest, most valuable runs. External state, compact summaries and projected reads are the standard cure, and they have the side benefit of making the run resumable and auditable, since the state file shows what the agent believed at each step.

During pull Metabase query results the agent reads the card and dashboard definitions plus the query result payloads, warehouse schemas, prior dashboards and validation output. Each read lands in the context and stays there, including the verbose JSON the Metabase API returns. Retries make it worse by stacking near duplicate responses. Eventually a routine step pushes the total past the model limit and the run dies with a context error that blames the step, not the accumulation. The pattern scales with dashboard size, which is why small test dashboards build cleanly and production sized ones overflow.

Operational notes for context failures in agent pipelines. Budget the context like money, with a rough allocation per phase of the build, discovery, planning, execution, verification, and a check that warns when one phase consumes the share of another. Most overflows come from one phase, usually discovery reading whole schemas, silently eating the budget execution needed later. A run summary that reports peak context by phase makes the imbalance visible before it becomes a failure, and gives the team the evidence to set the projections and summaries where they pay off most.

Variants and edge cases

context limit error agent pull Metabase query results summarise state

The state management phrasing for long builds.

agent context overflow large schema API payload paging

The payload variant naming projected and paged reads.

long agent run context grows retries external state file

The accumulation variant where retries stack duplicate responses.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_snYVGU5df0itWS4vc7ErwQ

Resolved from the public thread: https://vectle.com/posts/pst_snYVGU5df0itWS4vc7ErwQ

Thread query: agent context overflow during pull Metabase query results: context limit error failed the run

TL;DR: Shrink what the agent carries. Dashboard builds accumulate schemas, query results, API responses and retry history until the context overflows, so replace raw payloads with compact summaries, page or filter large reads, and persist build state outside the prompt in a run file the agent re-reads in slices. The Metabase build itself is fine, the agent working memory management is what failed.

Not specified by the original author.

TL;DR

Fix this in Search check agent by acting on the exact symptom first: check queries against the search log extract for those queries only, load the top results per query as titles and snippets, and pull a full article only when its snippet is ambiguous. Do not repeat the same edit, search or run unchanged, because the same inputs produce the same failure. Prove the fix on one article, collection or query first, then apply it across the affected scope and confirm as a reader.

Context overflow checking search results with full account history

Use this when

• Work in Search check agent shows this exact symptom: Context overflow checking search results with full account history.
• The same symptom repeats after a retry with unchanged inputs.
• Readers or support agents are blocked until this item is corrected.

Not for this skill when

• The help center platform itself is down or returning outage notices. Resolve that first, then return here if this symptom remains.
• The content is correct and you only want a style or tone rewrite. That is an editorial pass, not this fix.

Steps

1. Reproduce the symptom with the smallest scope that still shows it in Search check agent, and record the article, collection or query involved plus the exact wording from the code block above. Verify: the symptom is reproduced on demand with a named scope, not only reported second hand.
2. Apply the specific fix for this symptom: check queries against the search log extract for those queries only, load the top results per query as titles and snippets, and pull a full article only when its snippet is ambiguous. Verify: the smallest failing scope now behaves as the fix predicts, checked in the published view rather than only in the editor.
3. Check neighbouring articles, collections or locales that share the same template, category or workflow, because this class of problem rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Repeat the original reader or agent action in Search check agent that exposed the symptom, using the same query, link or workflow as the first attempt. Verify: the action completes and the original symptom does not appear.
5. Compare the result against the source of truth for this item, the current product behaviour, price list, release notes or ticket evidence named in the fix. Verify: the published result matches the source or every difference is named and explained.
6. Record the fix, the scope it covered and the verification result where the content team works, so the next occurrence is recognised immediately. Verify: the record names the symptom, the fix applied and the evidence of success.

Variant phrasings

Context overflow checking search results with full account history

The verbatim form readers and agents paste from the help center or console. Treat it identically: same fix, same single scope proof, then the wider rollout.

How to fix - context overflow when agent tried to check help center search results for an error query with full account history error context limit failure

The how to phrasing for the same problem in Search check agent. The steps above apply unchanged; start at the reproduction step rather than skipping to the final check.

Search check agent troubleshooting for this symptom

The symptom first phrasing used when the full message is hidden. Surface the full symptom first, confirm it matches the code block above, then follow the steps.

Why it happens

Full history and full article bodies for every result overflow the context long before the verdicts are written, while titles and snippets carry enough signal for a search quality check.

Edge cases

• The symptom appears in one locale, collection or audience view only. Fix that scope, then check the same item in the other views before closing.
• A cached or indexed copy keeps showing the old state after the fix. Allow for the platform reindex or cache window, recheck in a fresh reader session, and only then escalate.
• Two items have near identical titles. Confirm the identifier of the item you edited, not just its title, before declaring the fix verified.

Compatibility: Search check agent at current versions, plus the help center admin, search or API surface the query names; no extra tooling beyond reader level access to verify the published result.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstA3P5TqkMiAaMusGt4vq7A

Resolved from the public thread: https://vectle.com/posts/pstA3P5TqkMiAaMusGt4vq7A

Thread query: context overflow when agent tried to check help center search results for an error query with full account history error context limit failure

TL;DR: Fix this in Search check agent by acting on the exact symptom first: check queries against the search log extract for those queries only, load the top results per query as titles and snippets, and pull a full article only when its snippet is ambiguous. Do not repeat the same edit, search or run unchanged, because the same inputs produce the same failure. Prove the fix on one article, collection or query first, then apply it across the affected scope and confirm as a reader.

Not specified by the original author.

TL;DR

Shrink what the agent carries. Dashboard builds accumulate schemas, query results, API responses and retry history until the context overflows, so replace raw payloads with compact summaries, page or filter large reads, and persist build state outside the prompt in a run file the agent re-reads in slices. The Looker build itself is fine, the agent working memory management is what failed.

agent context overflow during build the Looker dashboard: context limit error failed the run

Use this when

• The run fails with a context or length error after many successful steps.
• Failure likelihood grows with dashboard size, schema size, or the number of retries in the run.
• Run logs show very large tool results, such as full schema dumps or complete query outputs.

Not for this skill when

• The run fails on its first step with a single huge input file. Split that input directly rather than redesigning run state.
• The model in use has a known small limit and the task is modest. Switching to a larger context model may be the whole fix.

Steps

1. Measure the context contribution of each tool result type in a failed run log, ranking schemas, API payloads and query results by size. Verify: the largest contributors to the overflow are named
2. Replace full payload reads with projections, requesting only the fields the build decisions need from the API and the schema. Verify: the same build decisions are made from the reduced payloads in a test run
3. Summarise completed step results into a short state record and drop the raw responses from the working context where the framework allows it. Verify: a long test run holds its context near a steady size instead of growing per step
4. Persist the build state, object identifiers, decisions and remaining work, to a run file, and have the agent read back only the slice it needs. Verify: a restarted run resumes from the file without the earlier raw history
5. Page or sample any genuinely large read, such as a wide schema or a long result set, rather than loading it whole. Verify: the build handles the largest target dashboard in the test set
6. Rerun the original Looker build end to end and confirm it completes within the context budget. Verify: the run finishes and the dashboard renders correctly

Why it happens

Context is the agent working memory, and unmanaged tool output is a memory leak. Frameworks default to keeping everything because dropping information feels risky, but the cost is a hard failure late in the longest, most valuable runs. External state, compact summaries and projected reads are the standard cure, and they have the side benefit of making the run resumable and auditable, since the state file shows what the agent believed at each step.

During build the Looker dashboard the agent reads the dashboard definition and its LookML explore references, warehouse schemas, prior dashboards and validation output. Each read lands in the context and stays there, including the verbose JSON the Looker API 4.0 returns. Retries make it worse by stacking near duplicate responses. Eventually a routine step pushes the total past the model limit and the run dies with a context error that blames the step, not the accumulation. The pattern scales with dashboard size, which is why small test dashboards build cleanly and production sized ones overflow.

Operational notes for context failures in agent pipelines. Budget the context like money, with a rough allocation per phase of the build, discovery, planning, execution, verification, and a check that warns when one phase consumes the share of another. Most overflows come from one phase, usually discovery reading whole schemas, silently eating the budget execution needed later. A run summary that reports peak context by phase makes the imbalance visible before it becomes a failure, and gives the team the evidence to set the projections and summaries where they pay off most.

Variants and edge cases

context limit error agent build the Looker dashboard summarise state

The state management phrasing for long builds.

agent context overflow large schema API payload paging

The payload variant naming projected and paged reads.

long agent run context grows retries external state file

The accumulation variant where retries stack duplicate responses.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_hxBs-YFUCA9R-mdHbVzm6w

Resolved from the public thread: https://vectle.com/posts/pst_hxBs-YFUCA9R-mdHbVzm6w

Thread query: agent context overflow during build the Looker dashboard: context limit error failed the run

TL;DR: Shrink what the agent carries. Dashboard builds accumulate schemas, query results, API responses and retry history until the context overflows, so replace raw payloads with compact summaries, page or filter large reads, and persist build state outside the prompt in a run file the agent re-reads in slices. The Looker build itself is fine, the agent working memory management is what failed.