resolve_date_range

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 explains key behaviors: the tool runs server-side for consistent time calculations, handles various natural language expressions, returns JSON with specific fields, and the output can be directly used in other tools' date_range parameters. It doesn't mention error handling or edge cases, but covers the core operational behavior well.

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, why needed, usage workflow, args, returns, examples) but is somewhat lengthy. Every section adds value, though some redundancy exists between the workflow explanation and examples. The information is front-loaded with the core purpose first.

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 (natural language parsing with server-side time calculation), no annotations, and the presence of an output schema, the description is remarkably complete. It explains the tool's purpose, workflow, parameter details, return format with examples, and integration with other tools. The output schema existence means the description doesn't need to exhaustively document return values, which it handles appropriately.

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 and only one parameter, the description fully compensates by providing extensive parameter semantics. It explains that 'expression' accepts natural language date expressions, lists multiple supported formats with examples (single days, weeks, months, recent N days, dynamic ranges), and shows both Chinese and English examples. This goes far beyond what the bare schema provides.

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: '将自然语言日期表达式解析为标准日期范围' (parse natural language date expressions into standard date ranges). It specifies the exact verb ('解析' - parse) and resource ('自然语言日期表达式' - natural language date expressions), and distinguishes itself from siblings by focusing on date parsing rather than analysis, search, or other operations.

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 guidelines: it recommends prioritizing this tool ('推荐优先调用') and outlines a specific workflow where AI should call this tool first to get consistent date ranges before using other tools like analyze_sentiment or search_news. It clearly states when to use it (for natural language date expressions) and provides concrete examples of the workflow.

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