zuckerbot_get_performance

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

No annotations are provided, so the description carries full burden. It details the output structure for legacy vs Intelligence campaigns but does not explicitly state that the operation is read-only or safe. It implies non-destructive behavior by describing metrics retrieval, but lacks explicit safety disclosure.

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 two sentences, front-loaded with the core purpose and immediately followed by concrete detail on output differentiation. Every sentence adds value with no 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 only one parameter and no output schema, the description fully covers the expected behavior: it explains what metrics are returned for both legacy and Intelligence campaigns, including AI recommendations. The use case is explicitly stated, making it complete for an agent to use correctly.

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 only parameter is campaign_id, which the schema already describes as 'ZuckerBot campaign ID.' The description does not add extra meaning or format details beyond what the schema provides. With 100% schema coverage, baseline is 3.

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 fetches real-time performance metrics for a ZuckerBot campaign, distinguishing between legacy and Intelligence campaigns with specific metrics listed. It also provides a use case ('monitor an active campaign or diagnose underperformance'). This differentiates it from siblings like zuckerbot_get_campaign or zuckerbot_get_campaign_insights.

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 to monitor an active campaign or to diagnose underperformance,' giving clear guidance on when to use it. It doesn't directly state when not to use it or mention alternatives, but the context is sufficient for appropriate selection among sibling tools.

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