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 reveals exactly what the tool returns (opaque plan_id, change count, preview URL, reasoning) and dictates a mandatory preview step before execution, which is meaningful behavioral context beyond the schema. It does not mention side effects or permissions, but for a planning tool this is reasonably transparent.

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 four sentences, each serving a distinct purpose: introducing the tool, providing examples, stating scope, and detailing return values and workflow. It is front-loaded with 'MASTER KEY' and is tightly written without filler, earning a high score for conciseness and structure.

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?

The description explains return values (plan_id, change count, preview URL, reasoning) even though no output schema exists, which is essential. It also mentions the target kits (Button, Cart, Badge, Variant, Frosty Pill) and the preview/execute flow. The main gap is the lack of guidance on the 'context' parameter's nested fields, which slightly reduces completeness for an agent needing to construct valid input.

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 schema description coverage is 50% (only 'intent' has a description). The description adds value by offering multiple examples of intent ('make my buttons feel premium', 'wellness brand vibe', 'BFCM aggressive'), which clarifies the expected natural-language format. However, the 'context' object and its nested fields (theme, market, season, products, brand_hint, constraints, current_settings) are left completely unexplained in both the schema and the description, leaving a significant semantic gap.

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 uses a specific verb ('analyzes') and resource (natural-language merchant intent → plan of settings changes) with concrete examples ('make my buttons feel premium', 'wellness brand vibe', 'BFCM aggressive'). It clearly distinguishes itself from siblings by claiming to be the 'MASTER KEY' tool that spans multiple kits and returns a structured plan, setting it apart from diagnose, recommend, or browse.

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 instructs 'ALWAYS call preview() before execute()', providing a clear workflow constraint. It also notes the tool handles both single tweaks and full overhauls with the same call, indicating scope. However, it does not explicitly contrast with alternatives (e.g., when to use diagnose vs plan), so it lacks full exclusionary guidance.

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