logs_volume

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

No annotations are provided, so the description carries full disclosure burden. It does reveal behavioral mechanics (bucketed counting, spike multiplier relative to a baseline, abnormal start-time detection), which adds value. However, it omits output shape, how the baseline is derived, pagination, and data limits — notable gaps given the absence of both annotations and an output schema.

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?

The description is tightly packed in two sentences — one defining the algorithm components (bucket count, baseline multiplier, abnormal start time) and one framing the diagnostic question answered. It is front-loaded and free of filler, though its brevity trades away useful detail that would help the agent.

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?

Given 8 parameters, no annotations, and no output schema, the description is comparatively thin. It conveys the core purpose and algorithm but leaves unclear how the baseline is established, what the result structure looks like, whether returned timestamps are relative to the input range, and how filter parameters influence detection. More elaboration is warranted for this complexity level.

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 only 25% (2 of 8 params described). The description implicitly connects the interval param to bucketed counting and range to trend viewing, but leaves labels, keyword, project, datasourceUid, start, and end unexplained. The description does not compensate adequately for the missing schema documentation on six parameters.

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?

The description clearly states the tool detects log volume trends and spikes via bucketed counting, baseline multiplier ratios, and abnormal start times, ending with the question it answers ('when did the problem start'). This distinguishes it from raw query (query_logs), baseline comparison (compare_baseline), and error summarization (summarize_errors) siblings, though the verb is implied rather than explicit.

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?

The description implies use for temporal diagnosis ('回答什么时候开始出问题') but provides no explicit when-to-use or when-not-to-use guidance. With 14 siblings including closely related tools like compare_baseline and query_logs, there is no direction on which to prefer for trend visualization vs. raw retrieval, nor any exclusion criteria.

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