lookup_ean

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

With no annotations provided, the description carries full disclosure burden and does so thoroughly. It reveals multi-step fallback behavior, prioritization of the bare EAN, optional role of product_hint, return format (candidates with title/URL), and a crucial guardrail: the agent must never claim a candidate is the product without merchant confirmation. This is rich, safety-relevant behavioral context.

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 compact, front-loaded with purpose, and every clause adds value: sources, fallback reason, parameter constraint, and agent guardrail. Despite its length, it is dense and free of fluff, making it both concise and structured for quick parsing.

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?

Since there is no output schema, the description compensates by explaining the return shape (candidates with title/URL) and the need for merchant validation. It covers tool scope, data sources, fallback logic, and a critical safety instruction, making it complete for an agent to invoke correctly within the given sibling context.

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?

The input schema already provides 100% coverage for both parameters, describing the EAN length and product_hint's role. The description adds context about sources but largely repeats the product_hint constraint already present in the schema. Therefore, no significant new parameter-level meaning is added beyond the schema baseline.

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 opens with a clear verb-resource pair: 'Busca dados de referência de um produto pelo código EAN/GTIN' (Looks up reference data for a product by EAN/GTIN code). It further distinguishes itself by detailing two sources (Open Food Facts and web search) and sets it apart from sibling tools like search_images or analyze_url, which target different actions.

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 a fallback sequence: use Open Food Facts first, then fall back to web search when the EAN is not in databases, with context about why (common for fashion/private label BR). It gives clear guidance on product_hint usage and explicitly warns not to override the EAN with it. No explicit alternative tool comparison, but the context is sufficient for deciding when to invoke this tool.

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