Suggested prices & predicted impact

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

Beyond the readOnly/idempotent annotations, the description discloses important behavior: rows will be empty without Market Insights opt-in, price amounts are in micros and may be strings (int64), and the meaning of predicted_*_change_fraction is explained (0.05 = +5%). This adds substantial context beyond the annotations and schema.

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, tightly packed with information: what it is, what it returns, and two important caveats (opt-in and micros/string format). No fluff or redundancy, and the key purpose is stated upfront.

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?

Despite no output schema, the description explains the output fields (suggested_price, predicted_*_change_fraction, effectiveness bucket) and behavioral edge cases (empty rows without opt-in). It also gives an example of the change fraction format. This is sufficient for an agent to correctly invoke the tool and interpret results.

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 descriptions cover 100% of parameters (account, page_size, page_token) with clear explanations, so the baseline is 3. The tool description does not add additional parameter-level detail, but it provides context that page_token is used for pagination, which is already in the schema. No extra value beyond schema.

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 function: a wrapper that returns Google's suggested_price and predicted impact (impressions, clicks, conversions) per product. It distinguishes itself from siblings like price_competitiveness by specifying it's a canned MCQL query over price_insights_product_view. The title 'Suggested prices & predicted impact' reinforces the verb+resource structure.

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 implies when to use this tool (as a convenience wrapper over a canned MCQL query) and provides a key prerequisite (Market Insights opt-in required). However, it does not name explicit alternatives or exclusions, such as using search_reports for custom queries, so it lacks the explicit 'use X instead' guidance found in top-tier examples.

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