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

No annotations are supplied, so the description carries the full disclosure burden, yet it never states what the tool actually emits (a generated plan? a template pack? prose?), whether the run is deterministic, or what the 'query' influences. The '30-day support promise' and 'ships as a prompt kit' are commercial framing rather than behavioral traits an agent can act on.

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?

Three sentences, but roughly half the text is promotional ('AI-powered', 'no prompt engineering needed', '30-day support promise') that does not help an agent decide or invoke. The one substantive clause about the new product launch workflow is buried behind the pitch instead of leading.

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 no-annotation, no-output-schema tool with a single optional parameter, the description must tell the agent what comes back and under what conditions to call it; it does neither. It also fails to differentiate from near-identical launch/ops siblings such as restaurant-newstore-launch, leaving selection ambiguous.

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 description coverage is 100% and there is only one optional parameter, so the schema already explains that 'query' scopes the analysis. The description adds nothing about how the query shapes the generated launch workflow or whether omitting it produces a generic default. Baseline 3 applies when the schema does the work.

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 does name a verb and a resource ('generate a ready-to-use fnb new product launch workflow'), so an agent can grasp the rough intent. However, it is prefaced by marketing framing ('AI-powered restaurant & brand intelligence pack', 'full prompt kit') that muddles whether the tool generates content or retrieves a static asset, and it draws no line against the many fnb siblings (menu-engineer, roi-gate, site-selector, store-diagnostic).

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?

There is no when-to-use guidance, no prerequisites, and no routing to alternatives despite a dense sibling cluster of fnb-* and restaurant-* tools. The phrase 'no prompt engineering needed' is a sales claim, not a usage condition. An agent must guess whether this tool applies to launching a dish, a SKU, or a whole store.

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