affiliate_get_brand_action_bundle

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context about the entitlement gate ('paid brand-data tool gated by the local entitlement check') and the failure mode ('returns a structured entitlement_required result rather than data'). It also clarifies what the bundle contains and what it excludes, but it doesn't explicitly state side effects or whether the tool is read-only, leaving a small gap.

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 information-dense and front-loaded with the primary purpose, followed by usage guidance, exclusions, and entitlement behavior. Each sentence earns its place, though it is slightly longer than necessary. The structure is logical and scannable.

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 one simple parameter and no output schema, the description is quite complete: it explains the bundle contents, usage context, prerequisites, exclusions, and entitlement behavior. It doesn't detail the exact output structure or return format, but it provides enough for an agent to select and invoke the tool with confidence.

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 has a single 'brand' parameter with 0% schema description coverage, so the description must compensate. While the description mentions the brand's strategy, networks, and snapshot, it doesn't explicitly define the 'brand' parameter's format or accepted values (e.g., ID vs. name). The meaning is inferable from context, but the description doesn't add specific parameter-level semantics beyond what's obvious.

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 identifies the tool's action ('Assemble the input bundle') and its resource ('brand AI deliverables'), enumerating the specific components it includes. It distinguishes itself from sibling tools by noting it excludes raw transaction rows, making its purpose distinct and well-defined.

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 states when to use this tool ('Use this to hand Claude one clean, structured input for a client-ready write-up') and provides a prerequisite ('build a snapshot first with affiliate_build_brand_snapshot'). It also gives a clear when-not by noting the deliberate exclusion of raw transaction rows, effectively steering agents away from using it for raw data needs.

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