zuckerbot_creative_analysis

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

With no annotations, the description carries the disclosure burden. It explains the return content: per-group CPL/CTTR/CPC/frequency averages, trend direction, structured recommendations, and optionally top/bottom adds. This gives a helpful expected-behavior contract for a read-oriented analysis tool. It doesn't't disclose whether the operation is strictly read-only, limited, or any edge-case handling, but for an analysis tool it's usefully transparent.

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 composed of three purposeful, front-loaded sentences: the tool's action, the output, and its recommended use in the workflow. It could be tightened (not enum values already in the schema with slightly redundant lists), but it remains efficient and well structured.

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?

Output description is present because there is no output schema, and it covers the most important return components. Given the tool has 11 optional params and 1 required, the combination of the `group_by` intro and high-coverage schema gives a complete context for most calls. It doesn't mention `cohort`, `summary_mode` implication in the main description, but their schemas already describe those behaviors.

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 covers all 11 parameters completely, including enums and meanings, so the baseline applies. The description does not add substantial per-parameter meaning beyond schema: it largely paraphrases `group_by` and the metric set. That is acceptable, but leaves gumption at baseline.

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 uses a specific verb and resource ('Analyse creative performance patterns... grouping ads by...') and enumerates grouping and metric options. It clearly communicates what the tool does. However, it does not explicitly distinguish itself from `zuckerbot_created_cross_analysis`, similarly creative performance name the same resource family, so it doesn't fully earn the top cluster on differentiation.

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 says 'Use this before generating new brief so inform the creative strategy.' This is clear contextual when-to-use instruction. However, it does not say when-to-use vs the sibling trools or explicitly exclude alternatives when they might be better fits.

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