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. It effectively describes key traits: the tool uses Jina AI Reader for conversion, removes noise content, has rate limits (100 RPM, 5-second intervals), and may fail on restricted pages. It also specifies the return format (JSON with Markdown). However, it lacks details on error handling or timeout behavior beyond the default.

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 appropriately sized and well-structured, with a clear purpose statement upfront, followed by usage guidelines, workflow examples, parameter details, and notes. Every section adds value without redundancy, and it uses bullet points and formatting (like **典型使用流程**) for readability.

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 (web scraping with rate limits), no annotations, and an output schema (which handles return values), the description is complete. It covers purpose, usage, parameters, behavioral traits (like rate limits and limitations), and integration with sibling tools, leaving no significant gaps for an AI agent to understand and invoke the tool correctly.

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 fully. It adds significant meaning beyond the bare schema: it explains that 'url' is required and must start with http:// or https://, and that 'timeout' has a default of 30 seconds and a maximum of 60 seconds. This provides clear semantic context that the schema alone does not.

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 with specific verbs ('读取' - read, '返回' - return) and resources ('文章内容' - article content, 'Markdown 格式' - Markdown format). It distinguishes from siblings by focusing on reading individual articles rather than searching, aggregating, or analyzing news, which are handled by other tools like search_news or analyze_data_insights.

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 guidance on when to use this tool versus alternatives, including a '典型使用流程' (typical workflow) that recommends using search_news first to get URLs and then read_article for content extraction. It also lists suitable scenarios ('适合用于' - suitable for) like reading news content or analyzing articles, and notes exclusions for paywalled or login-required pages.

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