Retriever query status

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations cover read-only/idempotent/open-world, but the description adds substantial behavioral context beyond them: the diagnostic state taxonomy (`dispatcher_failure`, `results_not_uploaded`, `observability_disabled`), the classification heuristic (scanned=0 with submittedTasks>0), the kubectl dependency and its host-reachability caveat, and the CloudWatch filter gated on a configured `queryLogGroup`. It also discloses the side effect of `fetch_results` pulling data from S3.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

It is one very long paragraph, which is dense but unstructured, and reads as a wall of text. It is front-loaded well with the core purpose, and nearly every clause carries operational information, so the length is largely earned rather than padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a post-hoc diagnostic tool with an output schema covering return values, the description supplies everything an agent needs: when to call it, how to obtain the required id, the diagnostic categories it produces, and the optional kubectl/S3 dependencies. No meaningful gap remains.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds meaning beyond it: how `query_id` is obtained (returned from `log10x_retriever_query` or recovered from the S3 results prefix), what `fetch_results` actually returns (10-event preview, counts, results_location), and that `include_pod_logs` triggers a kubectl call requiring host access.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

"Post-hoc diagnostic surface for a specific retriever query, addressed by queryId" gives a precise verb, resource, and addressing key, and the enumeration of what it pulls (coordinator `_DONE.json` stats, byte-count markers, result JSONL, CloudWatch events, diagnostics engine) makes the scope unmistakable. It is clearly differentiated from the submit-side sibling `log10x_retriever_query` and from `log10x_retriever_probe`/`log10x_retriever_series`.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit trigger conditions are given: "Call this whenever `log10x_retriever_query` returns 0 events but the wall-clock implies scan work, or whenever an offline post-mortem needs the structured timeline for a specific queryId." It also states when to flip `fetch_results: true` (recover stranded results after a partial/timed-out query without resubmitting) and when `include_pod_logs` applies.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.