predict_creative

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

With no annotations, the description must fully disclose behavior. It clarifies the tool is observational and non-forecasting, implying read-only access. However, it does not explicitly state side effects, authentication needs, or output details beyond 'fit score and tag associations.' This is adequate but not thorough.

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 with three sentences, each serving a purpose: context, functionality, and usage. It is front-loaded and efficient, though could slightly expand on the missing brand_name without becoming overly long.

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 tool with 3 parameters, no output schema, and no annotations, the description is moderately complete. It explains the core action and usage pattern but omits output format details and the meaning of brand_name. This leaves the agent with unanswered questions.

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 description adds minimal value over the input schema: it restates the alternative between analysis_id and attributes and gives an example for attributes. However, the required brand_name parameter is completely unmentioned, and schema coverage is only 67%. The description fails to explain brand_name, leaving a significant gap.

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: 'Compare a creative's tags with the brand's historical tag-level performance.' It specifies that it is not a forecast or causal estimate, distinguishing it from prediction tools. It also explicitly mentions forming a hypothesis with predeclared criteria, setting it apart from sibling tools like analyze_creative.

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: it is a 'Legacy-named observational pre-flight' used to form controlled-test hypotheses. It instructs to 'Pass an analysis_id or raw attributes.' However, it does not explicitly state when not to use this tool or compare to alternatives like analyze_creative.

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