read_article

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, and it does so well. It discloses the underlying Jina AI Reader service, automatic noise removal, the free-tier 100 RPM limit, built-in 5-second rate control, and the fact that paywalled/login-walled pages may not be fully retrievable.

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 sections for purpose, use cases, workflow, arguments, return format, and limitations. Every section adds practical information for an agent, and the primary 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?

The description covers the core return value (JSON containing Markdown), the expected usage flow, rate limits, and failure caveats. For a small two-parameter tool, this is sufficient for an agent to select and call it 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%, but the description compensates fully. It specifies that url is required and must start with http:// or https://, gives timeout semantics with default 30 and max 60, and includes a concrete example call.

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 states a specific verb and resource: reading the content of a given URL and converting it to LLM-friendly Markdown. It clearly differentiates from search-oriented siblings by focusing on fetching and cleaning page content rather than discovering or aggregating news.

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 explicit context: use after search_news(include_url=True), and for reading news正文, getting article details, or analyzing content. It does not explicitly contrast with read_articles_batch, so it lacks a direct exclusion for multi-URL cases, but the intended workflow is clear.

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