Suggested prices & predicted impact

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

The annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is clear. The description adds useful context about how the wrapper works (canned MCQL query) and warns about empty rows without opt-in, which the annotations do not cover. However, it doesn't disclose other behavioral traits such as no pagination details beyond the standard schema, or potential rate limits, but given the strong annotation coverage, the added context justifies a 3.

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 a single focused paragraph that front-loads the core purpose (wrapper over canned query and what it returns) then adds the key caveat and a data format note. Every sentence earns its place, though it could be slightly more scannable with bullet points.

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 read-only wrapper with 3 fully-described parameters and 100% schema coverage, the description does well. It explains the return structure (predicted_*_change_fraction, effectiveness bucket) and the opt-in requirement. However, it omits details on how the effectiveness bucket is computed or what to do if the opt-in is missing (it only says rows are empty), and there's no output schema to lean on. The absence of those details is a minor gap.

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 descriptions cover all 3 parameters (account, page_size, page_token) with 100% coverage, including the default behavior for account and the requirements for page_token. The description doesn't add additional parameter-level details beyond what's in the schema, so a baseline 3 is appropriate.

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 it is a convenience wrapper over a canned MCQL query on a specific view, and lists exactly what it returns: suggested_price, predicted change fractions, and an effectiveness bucket. This distinguishes it from the sibling search_reports and price_competitiveness by specifying the data source and the canned nature. It is specific and action-oriented.

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 mentions a prerequisite (Market Insights opt-in) that must be met for non-empty rows, and it implies this is the tool to get suggested pricing data from the product view. It doesn't explicitly name alternatives that should be used instead, but the canned nature and specific view suggest that search_reports or raw_request could be alternatives for more custom queries, though this is not stated.

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