app_adoption

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

No annotations are provided, so the description carries the full burden. It discloses ranking by adoption percentage and optional category filtering, but does not clarify if percentages are real-time, cached, or how 'analyzed stores' is defined. Lacks depth on data freshness or limits.

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 concise, two short paragraphs with front-loaded purpose. No unnecessary words. The examples and parameter list are efficient and clear.

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?

With an output schema present, the description doesn't need to detail return values. It covers purpose, parameters, and filtering. Could add a note about the source of data (e.g., all Shopify stores or a subset), but complete enough for the tool's simplicity.

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 0%, so the description adds value. It explains that 'category' filters by specific categories (e.g., Email Marketing) and 'limit' controls top-N results from 1-200. This complements the schema which only has types and defaults.

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 returns adoption percentage of apps across Shopify stores, with examples (PayPal 99.6%, Klaviyo 24.9%). It distinguishes from siblings like app_vs_app and find_stores_using_app by focusing on overall adoption ranking.

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 usage for seeing app adoption percentages, but does not explicitly state when to use this tool vs alternatives like app_vs_app or list_apps. The optional category filter is mentioned, but no when-not-to-use guidance.

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