cls_query_range_metric

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it supports PromQL-compatible query syntax, returns metric trends over time, requires specific topic_id format (time-series metric topic ID, not log topic ID), and has region defaults. It also mentions error handling for topic_id. However, it doesn't cover rate limits, authentication needs, or pagination behavior, leaving some gaps.

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 well-structured with clear sections (purpose, parameters, scenarios, notes). It's appropriately sized for a 6-parameter tool with no annotations. Every sentence adds value, though the parameter section is detailed, which is necessary given the 0% schema coverage. It could be slightly more front-loaded by moving the purpose statement to the very beginning.

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 the complexity (6 parameters, 0% schema coverage, no annotations, but with output schema), the description is remarkably complete. It covers purpose, all parameters with semantics, usage scenarios, critical notes, and references to sibling tools. The presence of an output schema means the description doesn't need to explain return values. This provides everything needed for effective tool selection and invocation.

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?

The schema description coverage is 0%, so the description must fully compensate. It provides detailed parameter semantics in the '参数说明' section, explaining each of the 6 parameters with purpose, format, requirements, defaults, and practical usage notes (e.g., topic_id format ambiguity resolution, time conversion requirement). This adds substantial value beyond the bare schema.

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's purpose: '查询 CLS 指标数据（时间范围）' (query CLS metric data with time range). It specifies the verb (query), resource (CLS metric data), and scope (time range). It distinguishes from sibling tools like cls_query_metric (which likely doesn't have time range) and cls_search_log (which handles logs not metrics).

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 provides explicit usage guidance in the '适用场景' (applicable scenarios) section, listing three specific use cases. It also includes '注意事项' (notes) with critical instructions about time conversion, explicitly naming the alternative tool cls_convert_time. This gives clear when-to-use and when-not-to-use guidance.

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