web_get_article

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

With no annotations provided, the description carries the full burden of behavioral disclosure, and it does so richly. It reveals that the tool performs two internal HTTP requests, that the 'documents' field is currently always empty, and that for articles with many variants/alternatives/cross-sell entries the completeness of the list is uncertain. These non-obvious limitations help the agent set correct expectations and avoid relying on incomplete data.

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 well-structured: the first sentence states the output content, the second adds caveats about implementation, empty documents, and potential incompleteness. All sentences carry relevant information and there is no filler, although the long list of fields makes it slightly dense.

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 output schema exists and the description does a good job of listing returned fields and known limitations. However, it lacks a clear definition of the input parameter, and without explicit usage guidance an agent may not know whether to supply a webshop material number, an EAN, or an internal article number. The tool is simple, but the input semantics gap prevents full contextual completeness.

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 schema has 0% description coverage for the sole parameter, and the description does not compensate. It does not explain what format material_number should take, whether it is an internal article number, EAN, or supplier number, or how it maps to the output fields mentioned such as 'eigene Artikelnummer' or 'Herstellernummer(n)'. This ambiguity could lead to incorrect invocation.

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 specific verb and resource: 'Artikeldetails aus dem Webshop laden' (load article details from the webshop) and enumerates the exact fields returned, such as EAN, manufacturer numbers, category, own article number, technical attributes, images, cutting costs, and related article numbers. This clearly distinguishes it from sibling tools like web_search_articles or get_price_availability, which focus on searching or pricing rather than full article details.

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 usage context is only implied: the required material_number parameter and the phrase 'Artikeldetails laden' suggest this tool should be used when an agent already has a material number and needs detailed article information. However, there is no explicit statement of when to use this tool versus alternatives like web_search_articles, nor any exclusions or preconditions.

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