zuckerbot_audit_account

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

Discloses key traits: read-only, available on all tiers, and report saving behavior under certain conditions. However, with no annotations, it could provide more detail on dependencies or edge cases, but covers the essentials for a safe, read-only operation.

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 three concise sentences, front-loaded with the core purpose and outputs. Every sentence adds value without redundancy.

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 lack of output schema and annotations, the description provides a solid overview of purpose, outputs, and side effects. It lacks detail on error handling or data source freshness, but adequately covers the tool's role and complexity.

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% with both parameters described. The description adds no new information beyond the schema's parameter descriptions, so a baseline score of 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 specifies the tool performs a full audit of a Meta ad account, detailing specific outputs like wasted spend detection, creative fatigue, opportunity score (0-100), and action items. It distinguishes itself from sibling tools by being a comprehensive first-call analysis.

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?

Explicitly recommends using this tool as the first call for new accounts or when users ask 'how are my ads doing?', providing clear usage context. However, it does not explicitly mention when not to use it, leaving a minor gap.

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