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 external service dependency (Jina AI Reader), rate limits (100 RPM), built-in rate control (5s interval), and limitations with paywalls. This is rich behavioral context beyond what schema or annotations would provide.

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 sections for usage, args, returns, example, and notes. Each section adds value, and there is no unnecessary fluff. It is appropriately sized for a tool with no annotations, covering all essential aspects without being overly verbose.

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 read tool with an output schema, the description covers purpose, usage flow, parameter constraints, return format, limitations, and rate limits. It is complete enough for an agent to select and invoke the tool correctly without ambiguity.

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. It adds meaning to both parameters: url is required and must start with http/https; timeout has default 30 and max 60. It also provides an example, making parameter usage very clear.

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: reads article content from a specified URL and returns LLM-friendly Markdown. It distinguishes from siblings by specifically targeting single-article reading, and contrasts with read_articles_batch for batch operations.

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?

Provides explicit use cases and a typical workflow: search_news first, then read_article. This gives clear context for when to use it. However, it does not explicitly mention alternatives or when NOT to use it (e.g., for batch reading), so it falls short of full exclusion guidance.

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