google_play_reviews

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

With no annotations, the description carries the transparency burden. It discloses that reviewer names and avatars are omitted by default because they are personal data, and explains the language/country variability. This adds meaningful behavioral context beyond the 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 two sentences, front-loaded with the core purpose, and every phrase adds value. It is tightly written without redundancy or irrelevant details.

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?

The description mentions the return fields, the personal data omission, and the language/country coverage tip, which collectively give a good sense of what to expect. It does not discuss sort/limit behavior, but those are already documented in the schema.

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 already covers 100% of parameters, so the baseline is 3. The description adds value by explaining the interplay between hl/gl and review coverage, and by clarifying why include_author defaults to false (personal data concern). This exceeds baseline.

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 fetching Android app reviews and enumerates the data fields (rating, text, date, app version, thumbs-up, developer reply). It lacks an explicit verb like 'get' or 'fetch', but the resource and scope are specific enough to distinguish it from sibling tools.

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 gives direct usage guidance: varying hl/gl to widen coverage and acknowledging that each language/country returns different reviews. It does not explicitly contrast with sibling tools or state when NOT to use it, but the context is clear.

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