read_article

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, and it does so well. It reveals the underlying Jina AI Reader service, automatic noise removal, 100 RPM limit, built-in 5-second rate control, and the limitation that paywalled pages may not be fully fetched. These are exactly the behavioral traits an agent needs to know beyond the schema.

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 minimal but well-structured with sections for use cases, workflow, Args, Returns, Examples, and Notes. The front-loaded first sentence immediately states the core function. A few phrases are somewhat redundant, such as repeating Markdown output, but overall the structure makes the information easy to scan and every major section earns its place.

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 tool with only two simple parameters and no annotations, the description is complete: it defines the input constraints, shows an example call, describes the return format, and warns about rate limits and paywall limitations. The only minor omission is not addressing the batch sibling, but that does not materially affect correct invocation of this tool.

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 compensate for missing parameter details—and it does thoroughly. It explains that url is required and must start with http:// or https://, and that timeout is in seconds with a default of 30 and maximum of 60. This adds meaningful semantic value beyond the bare input 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 a specific action ('读取指定 URL 的文章内容') and the return format ('LLM 友好的 Markdown'), making the tool's core purpose unambiguous. It also names relevant use cases like reading news and analyzing article content, which helps distinguish it from search-oriented siblings. However, it does not explicitly contrast itself with the sibling read_articles_batch, so cross-tool differentiation is slightly weaker than ideal.

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 a concrete typical workflow: use search_news(include_url=True) first, then read_article(url=链接), and follow with AI analysis. It also states suitable scenarios ('阅读新闻正文、获取文章详情、分析文章内容'). It does not mention when to avoid this tool or explicitly point to read_articles_batch for multiple URLs, but the guidance is clear enough for most agent decisions.

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