enrich_product

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

With no annotations, the description carries the full burden of behavioral disclosure. It does this well by highlighting the critical with_images behavior: the default produces lightweight JSON, while with_images=true embeds base64 and can balloon to 100k+ tokens. It also lists the full transformation pipeline, giving the agent a clear picture of what happens to the product. It doesn't mention side effects like API costs or rate limits, but the most important caveat is covered.

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 a single paragraph that conveys a lot of information without unnecessary filler. The 'IMPORTANTE' warning draws attention to the token implication. It is moderately dense but well-organized; breaking it into bullets would improve scannability, but it is still effective.

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 tool's complexity and the lack of an output schema, the description provides a solid high-level view of the output (storefront-ready product) and the enrichment steps. It also includes a crucial warning about output token size, which helps an agent manage context. The main gap is the absence of a detailed output structure, but the enrichment steps imply what to expect.

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 must compensate. It does a good job explaining the with_images parameter and its impact on output size. However, it does not explain with_ai or locale, leaving those to the schema defaults. It gives a high-level description of the product input ('raw ERP product') but does not detail its structure, which the schema already provides. Overall, partial compensation.

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 defines the tool as an enrichment pipeline with a specific list of actions (title sanitization, bullets, HTML description, SEO, JSON-LD, image lookup, attribute normalization). It frames the input as a raw ERP product and the output as a storefront-ready product, distinguishing 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 explicitly states the input (raw ERP product) and output (storefront-ready product), giving clear context for when to use this tool. It also provides parameter-specific guidance for with_images, explaining the default behavior and the token trade-off. However, it does not explicitly compare against alternatives like enrich_batch, so it falls short of a 5.

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