read_article

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

Despite no annotations, the description fully discloses key behavioral traits: uses Jina AI Reader, converts to Markdown, auto-removes noise, has 100 RPM rate limit, built-in 5-second interval, and potential limitations with paywalled pages. This exceeds the burden for unannotated tools.

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 (purpose, typical flow, args, returns, examples, notes) and front-loaded with the main action. While slightly verbose, each sentence adds value and aids comprehension.

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?

Given no annotations and an existing output schema, the description covers all necessary aspects: input parameters, behavior, rate limits, examples, and limitations. It provides a comprehensive understanding of the tool's operation and constraints.

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?

With 0% schema description coverage, the description adds crucial meanings: url must start with http/https, timeout defaults to 30 and max is 60. It explains the purpose of each parameter beyond the raw schema, fully compensating for the lack of property descriptions.

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 that the tool reads article content from a URL and returns LLM-friendly Markdown. It distinguishes itself from sibling tools like read_articles_batch by specifying single-article reading, and from search_news by being the subsequent step in the workflow.

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 a clear typical usage flow (search_news then read_article) and states appropriate use cases (reading news, analyzing content). However, it does not explicitly mention when NOT to use the tool or direct alternatives for similar tasks.

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