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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations: the tool reports subcategories rather than brands, ranks by private-label winnability, uses 2-year marketplace data, and can bias toward 'unhappy_shoppers' as a PL opening. This gives the agent a good sense of what the tool computes and returns.

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 dense but every sentence earns its place: it defines the output type, provides query-phrase examples, gives the category_name mapping, and names the sibling tools to avoid. The structure is front-loaded with the core definition and then progressively adds routing and exclusion guidance, with no filler.

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 the tool's complexity — 21 parameters, no output schema — the description covers the essential usage context: what the tool returns, how to scope by category_name, and which sibling tools to avoid. It does not specify the exact return structure or how multiple filters interact, but the main niche-discovery flow is thoroughly explained. For an unannotated output schema, this is strong but not exhaustive.

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 only 43%, so the description bears added weight for parameter semantics. It does provide valuable extra guidance: 'When the user names a department or category ... pass it as category_name to scope the niches to that area.' However, it does not compensate for the many undocumented numeric filter parameters (e.g., max_competing_brands, min_seller_diversity, min_monthly_demand_usd), leaving much interpretation to their self-explanatory names.

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 states a specific verb and resource: 'Find UNDER-SERVED NICHES' and explicitly defines the output as subcategories ranked by private-label winnability. It draws a clear line against sibling tools by stating 'Returns CATEGORIES / sub-categories ... NEVER brands', so an agent can immediately distinguish it from brand-returning tools.

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 gives explicit when-to-use guidance: 'This is the RIGHT tool for ANY niche-discovery question' followed by concrete utterance examples. It also names exclusions: 'Do NOT use category_undercompeted_brands or find_undercompeted_brands for niche questions — those return BRANDS', leaving no ambiguity about routing.

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