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 reveals that the tool uses server-side current time for calculation, which is a key behavioral trait ensuring consistency. It also details the return JSON structure with 'success', 'expression', 'date_range', 'current_date', and 'description', plus the supported expression categories. It does not cover error handling or timezone behavior, but for a date parsing tool, it is reasonably transparent.

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 longer than average but well-structured with clear sections (why needed, recommended flow, Args, Returns, Examples). Every section adds value: the 'why' explains the tool's raison d'être, and the examples illustrate exact usage. It is not overly verbose because the tool's behavior is inherently nuanced (supports many expression forms). A minor deduction for the extra length, but it earns a high score for effective organization.

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 single-parameter tool with no annotations and no output schema shown, the description is remarkably complete. It explains the purpose, the exact input format, the output structure with a concrete JSON example, and provides two full AI-call workflows. It covers all necessary aspects for an agent to select and invoke the tool correctly, leaving little ambiguity about the expected behavior and result.

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 provides zero description for the 'expression' parameter (schema_description_coverage is 0%). The description fully compensates by listing all supported expression types (e.g., '今天', '本周', '最近7天', arbitrary dynamic expressions) and providing examples. This gives the AI agent far more semantic understanding than the bare schema string field, making the parameter interface clear and actionable.

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 function: parsing natural language date expressions into standardized date ranges ('将自然语言日期表达式解析为标准日期范围'). It uses a specific verb (parse/resolve) and resource (date expressions), and the '推荐优先调用' label further emphasizes its role. This clearly distinguishes it from siblings like get_news_by_date or analyze_trends, which operate on dates rather than resolving them.

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 context through the '推荐使用流程' section, showing exactly when to call this tool (before other date-dependent tools like analyze_sentiment) and why (server-side time ensures consistency). It also includes worked examples. However, it does not explicitly state when NOT to use it (e.g., when a concrete date range is already available), so it misses the 'when-not/alternatives' element that would earn a 5.

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