AI app review analysis

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

Annotations already cover safety (readOnlyHint=true, destructiveHint=false, openWorldHint=true), so the description is not carrying that burden. It adds genuinely useful operational context the annotations cannot convey: 30-90 seconds per app, per-app cost of $0.05 ($0.04 on Gold), and the contents of the returned report. It does not cover failure modes or partial-result behavior.

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?

A single dense paragraph, well front-loaded: purpose and scope first, accepted input formats second, use cases third, and cost/latency last. Every sentence carries information, though the output enumeration and input-format list make it slightly long.

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?

There is no output schema, so the description usefully enumerates what the report contains, and it adds the latency and cost dimensions an agent needs for a long-running paid job. The main remaining gap is that it does not clarify the relationship to get_app_reviews or what happens when an app cannot be resolved.

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 description coverage is 100%, so all three parameters are already documented in the schema, and the description largely restates the accepted identifier formats for 'apps'. It adds no syntax or format detail beyond what the schema provides for country or max_reviews_per_app, so the baseline 3 applies.

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 names a specific verb+resource ('AI analysis of the latest App Store and Google Play reviews') and enumerates the exact analysis outputs (top bugs, feature requests, critical issues, competitor mentions, sentiment, rating distribution, versions). It is highly specific, but it never explicitly distinguishes itself from the closely related sibling get_app_reviews, which an agent must choose between.

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?

It states clear usage contexts ('product research, competitor analysis and release monitoring') and notes the scale limit of up to 5 apps, giving the agent a solid sense of when the tool applies. It stops short of naming when not to use it or pointing to get_app_reviews as the raw-data alternative, so the routing decision is left partly to inference.

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