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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so safety and scope are covered. The description adds useful behavior beyond that by enumerating exactly what is produced (three versions, matrix, ranked proof points, test questions), but it doesn't say how it behaves when customer_metrics is absent or whether it fabricates figures.

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?

Roughly seventy words for eight parameters, with the benefit statement front-loaded and inputs before outputs. The final sentence is a dense run-on list of deliverables, but no sentence is padding.

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?

With no output schema, the description correctly carries the burden of describing returns in detail, and it does. The gaps are the unmentioned your_sector parameter and unspecified handling of missing optional data (e.g., no customer_metrics), which matter for an 8-parameter generative tool.

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%, so the baseline is 3. The description recaps the three required inputs plus product name, category, customer results and business model, but conspicuously omits your_sector, whose enum and sector-notes effect are documented only in the schema.

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?

"Helps you decide what you can claim that buyers will believe" is a vague opener, but the description then names concrete artifacts: value statements in three versions, a value matrix, ranked proof points, audience/channel variants, and test questions. An agent can tell this is a value-proposition/proof-point generator rather than a messaging drafter or market anchor tool, though it never names the sibling it is contrasted with.

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?

Usage is only implied: it presumes you already have a target customer, a main outcome, and a differentiator. There is no explicit "use this when..." statement, no exclusions, and no pointer to impact_craft_message or impact_anchor_market for adjacent tasks, leaving the agent to infer routing from sibling names alone.

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