extract_shopify_store

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

With no annotations, the description carries full responsibility. It discloses the mechanism (reads products.json), the output structure (row per variant with listed fields), and behavior of parameters (save_as for storage, max_products for capping). It does not mention potential failure modes like missing products.json, but provides enough behavioral context for an agent to understand the tool's operation.

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: the first succinctly states the purpose and output format, the second explains the mechanism and key parameters. Every word earns its place, with no redundant or vague phrasing. It is front-loaded with the core action and concludes with practical guidance.

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 covers the essential aspects: what it does, how it works, key parameters, and a storage recommendation. An output schema exists, so return values need not be detailed. It lacks explicit error-handling or limitations (e.g., stores without products.json), but for a Shopify-specific extractor, it provides sufficient context for typical usage.

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 0%, so the description must explain all parameters. It does so effectively: store_url is implied as the store being extracted, save_as is explained as the table name for storage, and max_products is described as capping the number of products. All three parameters are given meaningful context beyond their names.

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 extracts a Shopify store's catalogue as one row per product variant, listing specific fields (price, sku, options, etc.). It distinguishes itself from siblings by mentioning it reads the public products.json and requires no rendering or selectors, which differentiates it from selector-based extraction tools like extract_by_selector and generic extract_products.

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 explains that it reads the store's public products.json, implying it works when that file is available. It also recommends using save_as for large catalogues, providing a practical usage tip. However, it does not explicitly state when not to use it or mention alternatives, though the context of Shopify-specific extraction is clear.

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