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. It discloses important behavioral traits: uses Jina AI Reader, strips ads/navigation noise, 100 RPM limit, built-in 5-second rate control, and possible paywall/login limitations. This is meaningful, though it could also mention error behavior or HTTP failure handling.

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-organized with clear sections, a typical workflow, examples, and notes. Every section provides useful operational information, and the core purpose is front-loaded.

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 two-parameter tool with an output schema present, the description covers the main call flow, parameters, output format, and operational constraints. It is complete enough for an agent to invoke the tool correctly, though it omits explicit error-handling expectations.

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%, but the description compensates well by explaining that url is required and must start with http:// or https://, and that timeout defaults to 30 with a maximum of 60 seconds. This adds practical meaning beyond the raw 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 uses a specific verb+resource ('读取指定 URL 的文章内容') and clearly states the output format (LLM-friendly Markdown). It also distinguishes the tool's purpose from search-oriented siblings by framing it as the follow-up step after searching for news links.

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 gives an explicit typical workflow: search_news first, then read_article, then analysis. This is strong contextual guidance, though it does not explicitly mention when to use the sibling read_articles_batch instead, so it falls just short of a 5.

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