get_news_by_date

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 by disclosing key behavioral traits: it explains the return format ('JSON格式的新闻列表'), includes performance considerations ('节省token' for token saving with include_url default), and specifies operational limits ('默认50，最大1000'). It doesn't mention rate limits, authentication needs, or error handling, but covers more than basics for a read operation.

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 a purpose statement, detailed Args section, and Returns section—all in a compact format. Every sentence earns its place by explaining functionality or parameters. It could be slightly more front-loaded by moving the purpose statement earlier, but overall it's efficient with minimal waste.

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 4 parameters, 0% schema coverage, no annotations, but an output schema exists, the description is largely complete. It thoroughly documents all parameters and their usage, explains the return format, and provides context for the tool's purpose. The output schema means it doesn't need to detail return values, but it could mention sibling tools or error cases for a perfect score.

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 description coverage is 0%, so the description must fully compensate—and it does excellently. For all 4 parameters, it provides clear semantics: date_range formats with examples, platforms as ID lists with examples, limit defaults and max, and include_url purpose and default. This adds substantial meaning beyond the bare schema, fully documenting parameter usage.

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 as '获取指定日期的新闻数据' (get news data for specified dates) with the specific use case '用于历史数据分析和对比' (for historical data analysis and comparison). It distinguishes from siblings like 'get_latest_news' (which presumably gets current news) by focusing on historical/date-specific retrieval. However, it doesn't explicitly contrast with 'search_news' or 'find_related_news', keeping it from a perfect score.

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 usage context through '用于历史数据分析和对比' (for historical data analysis and comparison), suggesting this tool is for retrospective analysis rather than real-time monitoring. However, it provides no explicit guidance on when to use this versus alternatives like 'search_news' or 'get_latest_news', nor does it mention any prerequisites or exclusions. The usage context is helpful but incomplete.

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