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

Annotations already mark the tool as read-only. The description adds value by stating it is a 'pure data fetch (no AI analysis)' and 'raw' data, clarifying that the output is unprocessed and requires manual interpretation. This goes beyond the simple readOnlyHint.

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 structured with a clear definition first, then a usage note and how-to instructions. It is slightly verbose but every sentence earns its place by conveying non-obvious details (e.g., no AI analysis, how to weigh metrics). Overall efficient and front-loaded.

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 3-parameter tool with no output schema, the description provides a solid overview of the returned data (distributions, concentration, brand spread) and the decision-making context. It could benefit from specifying exact output fields or examples, but it is adequately complete for an agent to understand what to expect.

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 covers 100% of parameters with descriptions, so the baseline is 3. The description does not add meaningful parameter-level detail beyond what the schema already provides; it focuses on output characteristics and usage, not parameter semantics.

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 states the tool's purpose: it fetches raw head-to-head data for 2–5 Amazon niches/category keywords, including per-niche product sets and computed aggregates. It also explicitly distinguishes itself from compareProducts (for ASINs), making the scope unambiguous.

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 provides clear context for when to use the tool (comparing niches with raw data) and names an alternative for ASINs (compareProducts). However, it does not explicitly mention when to use the tool over other sibling tools like analyze_niche, missing full when/when-not coverage.

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