read_article

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

No annotations are provided, so the description carries full burden. It discloses the use of Jina AI Reader, the stripping of ads/navigation, built-in rate control (5-second interval, 100 RPM limit), and potential failure on paywalled pages. This is rich behavioral context beyond what the tool name alone imparts.

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 multi-paragraph but well-structured with clear sections (main purpose, use cases, flow, args, returns, note). It is somewhat longer than strictly necessary, but every section adds value and it is front-loaded with the core purpose.

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?

With a rich description covering purpose, parameters, return type, rate limits, and limitations, it is complete for a tool with 2 parameters and an output schema. It does not explicitly differentiate from read_articles_batch, but the typical flow provides contextual guidance.

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 coverage is 0%, so the description must and does explain both parameters. It states url is required and must start with http(s)://, and timeout has a default of 30 seconds and max of 60. This adds meaningful meaning beyond the bare 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 the tool reads article content from a specified URL and returns LLM-friendly Markdown, specifying both the verb and resource. It distinguishes itself from siblings like read_articles_batch by focusing on singular article retrieval and providing a typical use case flow.

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 clear usage context, recommending it for reading news articles and analyzing content, and outlines a typical flow with search_news first. However, it does not explicitly mention when not to use it or direct users to alternatives like read_articles_batch for multiple articles.

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