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

The readOnlyHint annotation already covers safety, and the description adds valuable behavioral context: it pulls live data, runs analyses internally, and returns a user-ready report. This explains the orchestration behavior beyond a simple read operation, though it does not discuss limitations or edge cases.

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 three sentences long, front-loaded with the tool's role ('AMZScout all-in-one Amazon research assistant'), and every clause carries meaning—purpose, behavior, usage guidance, and output. No redundancy or 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 an open-ended natural language agent, the description provides sufficient context: what it does, how it behaves (internal analysis, no sub-tool selection), when to use it, and what it returns ('complete, user-ready report'). Even without an output schema, the return expectation is clear.

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 fully describes both parameters (message and history), so the description adds no additional parameter-level detail. It only reinforces that the message is a natural language request, which is already implied by the schema description. Baseline of 3 is appropriate given 100% schema coverage.

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 identifies the tool as an all-in-one Amazon research assistant that accepts natural language queries and returns a finished analysis. It distinguishes itself from the granular sibling tools by stating that no sub-tool selection is needed, making the purpose 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?

Explicitly states when to use this tool ('Best for a hands-off answer') and names the alternative ('the granular amzscout_* tools are the alternative when step-by-step orchestration is preferred'). This gives direct guidance on tool selection.

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