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

The description adds cost and sequencing context the annotations don't carry: this scan is free, while the follow-up upgrade_product_check costs 1 token. Annotations declare openWorldHint=true and readOnlyHint=false, which could make an agent hesitate on a 'scan'; the description's framing as a non-destructive catalog read partially offsets that but never resolves why readOnlyHint is false. Overall it adds meaningful value beyond the structured fields.

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?

Front-loaded with the value proposition, then output contents, then next steps and alternatives – a sensible ordering with no filler. It loses a point because the first sentence is a long run-on list that takes effort to parse and mixes output contents with purpose.

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 no output schema, the description must describe returns, and it does so concretely (three deficiency counts, store-wide AI visibility issues, prioritized fix list). For a one-parameter, free-tier entry point, an agent has everything needed to call it and to know what comes next.

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 coverage is 100% for the single store_url parameter, including an example URL format, so the schema does the heavy lifting. The description only restates 'by its address' without adding constraints such as normalization, http vs https handling, or error behavior for invalid URLs. Baseline 3 is appropriate.

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 and resource ('Free scan of a Shopify store's catalog') and immediately enumerates the outputs: products missing video, thin descriptions, fewer than 4 photos, robots.txt/structured-data issues, and a prioritized fix list. It also names the sibling it hands off to (upgrade_product_check) and the per-product alternative (check_product_page), so an agent can separate it from siblings without opening any schema.

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?

Explicit routing: use this for a store-wide Shopify catalog scan, use upgrade_product_check for the top 15 products' AI visibility, and use check_product_page per product for other platforms. The implicit exclusion of non-Shopify stores is stated outright ('Other platforms: use check_product_page'), leaving little to infer.

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