extract_structured

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description adds important behavioral details: default LLM usage and fallback to CSS selectors. This transparency aids agent understanding without contradicting annotations.

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 concise, front-loaded with purpose, and includes an illustrative example. Every sentence is informative, with no unnecessary fluff. It could be slightly more structured (e.g., bullet points) but is efficient overall.

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 the tool's complexity (6 parameters, nested schema object) and lack of output schema, the description omits return format and error handling. The example provides partial coverage, but the level of detail is adequate for typical use, leaving some gaps for edge cases.

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 100%, so the description does not need to repeat parameter details. The example demonstrates url and schema usage, but adds marginal new semantics beyond the schema definitions. Baseline 3 is appropriate.

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 it extracts structured data using a JSON schema, with examples like product details and job listings. It differentiates from siblings by emphasizing JSON schema input and fallback behavior, but could more explicitly contrast with similar tools like scrape_structured or extract_with_llm.

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 a clear usage context ('when you need a specific data shape extracted'), but lacks explicit guidance on when not to use it or alternatives. The example is helpful but does not cover exclusions or comparisons to sibling tools.

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