Modularen Willkommenstext generieren

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

No annotations are provided, so the description must disclose behavioral traits. However, it only states the output type ('welcome text') without mentioning side effects, permissions, rate limits, or whether it is read-only or destructive. The lack of specification is a significant gap.

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 sentence, which is concise. However, it is under-specified; it would benefit from additional context while remaining brief. It is not verbose, but minimalism sacrifices completeness.

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 complexity (nested objects, many subproperties, no output schema), the description is insufficient. It does not explain return values, what the 'module template system' is, or how to structure inputs properly.

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% (no descriptions on top-level parameters), and the tool description adds no information about the parameters. It does not explain what 'data' or 'moduleOptions' are or how to use them, despite the schema having nested objects and multiple subproperties.

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 verb ('generiert' = generates) and resource ('strukturierten Willkommenstext' = structured welcome text), and the tool name aligns with this purpose. It is distinct from sibling tools like 'extract_from_text' and 'list_extracted_data', which are about extraction and listing, not generation.

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 provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, when not to use it, or compare with sibling tools.

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