cls_get_log_count

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

With no annotations provided, the description carries full burden and does well. It discloses important behavioral traits: performance characteristics (faster than cls_search_log), prerequisite tools (cls_convert_time for timestamp conversion, cls_describe_index for SQL preparation), and error handling guidance for topic_id. It doesn't mention rate limits or authentication needs, but covers substantial operational context.

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 (参数说明, 适用场景, 注意事项) and uses emphasis (⏰, 💡) effectively. While comprehensive, it maintains focus with each sentence adding value. Some redundancy exists in parameter explanations, but overall it's efficiently organized.

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 tool's complexity (5 parameters, 0% schema coverage, no annotations) and presence of output schema, the description is remarkably complete. It covers purpose, usage guidelines, parameter details, prerequisite tools, error handling, and operational constraints. The output schema existence means return values don't need explanation, allowing focus on invocation context.

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?

With 0% schema description coverage, the description fully compensates by providing rich parameter semantics. It explains each parameter's purpose, format constraints (UUID or custom string for topic_id, Unix timestamp in milliseconds for time fields), optionality, and practical usage guidance including fallback strategies for topic_id resolution and region defaults.

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: '快速获取日志数量' (quickly get log count). It distinguishes from sibling cls_search_log by noting it's faster and suitable for scenarios where only the total count is needed. This provides specific verb+resource and sibling differentiation.

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 explicitly states when to use this tool ('适合只需要知道日志总数的场景' - suitable for scenarios where only total count is needed) and when to use alternatives ('相比 cls_search_log 更快' - faster than cls_search_log). It also provides specific usage scenarios like confirming log existence and counting logs in time ranges.

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