app_vs_app

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

No annotations provided, so description carries full burden. It describes the outputs well and implies a read-only query, but does not explicitly state its safety, idempotency, or lack of side effects. An explicit note would improve transparency.

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?

Description is well-structured with a clear purpose sentence, return list, usage advice, and parameter definitions. Slightly wordy in the return list, but front-loads key information and is easy to scan.

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?

Given the complexity of a comparison tool with multiple return metrics and an existing output schema, the description provides sufficient detail on what the tool returns and how to interpret the results. It is complete for an agent to understand and use correctly.

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 has 0% coverage (no descriptions). Description adds meaning with 'slug of the first app' and examples (e.g., 'klaviyo'), clarifying the parameter intent beyond just 'string'. However, no format constraints or validations are mentioned.

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?

Description clearly states it performs a head-to-head comparison of two apps' installed bases, with specific return metrics. It differentiates from siblings like app_adoption (single app) and compare_stores (stores) by focusing on two-app comparison.

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?

Provides context with competitive questions and highlights the most interesting metric (cross-install asymmetry). Lacks explicit when-not-to-use statements, but the context is clear and helps in selection.

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