read_article

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

With no annotations provided, the description carries full transparency burden. It discloses the underlying Jina AI Reader service, rate limits (100 RPM), built-in 5-second request delay, and paywall limitations. This is thorough and gives the agent realistic expectations.

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-structured with clear sections (Args, Returns, Examples, Notes) and is front-loaded with the main purpose. Every sentence adds value, and the markdown formatting enhances readability without redundancy.

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 simple two-parameter read tool, the description is complete: it covers the typical workflow, parameter semantics, return format, an example, and operational constraints. The presence of an output schema is acknowledged by the description's mention of JSON return format, making it sufficient for an agent to correctly invoke the 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?

The input schema has no descriptions for parameters (0% coverage), so the description fully compensates. It explains url is required and must start with http/https, and timeout has a default of 30 and max of 60. This adds essential 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's function: reads article content from a specified URL and returns LLM-friendly Markdown. It also references a workflow with search_news, distinguishing it from sibling news tools.

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 explicitly outlines a typical usage flow (search_news → read_article → analyze) and lists suitable use cases. However, it does not explicitly state when not to use the tool or mention batch alternatives like read_articles_batch, making it clear but not exhaustive.

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