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

Annotations declare readOnlyHint=true, and the description reinforces this with 'Pure data fetch (no AI analysis)', adding value beyond the annotation by clarifying the tool returns raw data requiring the caller to do interpretation. However, it doesn't detail output structure, pagination limits, or rate-limit behavior, leaving some transparency gaps despite the annotation coverage.

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?

Compact but dense single paragraph. Every sentence earns its place: purpose, AO distinction, usage guidance, and actionable framework all in a few lines. Slightly wordy toward the end with the 'how to use' guidance, but it's genuinely useful instruction rather than 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?

For a data-fetch tool with no output schema and good annotations, the description covers purpose, output contents, differentiation from siblings, and a concrete usage framework. It doesn't describe exact return shape (arrays, distribution stats format), but for a raw-data tool whose schema declares readOnly, this is reasonably complete.

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 coverage is 100%, so the schema already documents count, keywords, and marketplace with descriptions and constraints. The description adds the niche keyword format example ('yoga mat', 'resistance bands') and mentions the 2-5 range, but the schema already covers these. Baseline 3 is appropriate since schema does the heavy lifting.

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?

Clear specific verb+resource: 'head-to-head data for 2-5 Amazon niches'. Distinguishes from sibling amzscout_analyze_niche by explicitly noting 'Pure data fetch (no AI analysis) — do the comparison yourself'. Also contrasts with amzscout_compare_products for ASIN-level comparison, differentiating at the resource level.

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?

Explicitly states this is raw data fetching requiring the agent to do the comparison itself (vs analyze tools). Names the ASIN equivalent (compareProducts) and gives a concrete usage framework: weigh demand against competition and price, then give a verdict on conditions. This gives the agent clear context on when and how to use it.

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