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

With no annotations, the description must carry the full behavioral burden. It discloses the question count, automatic language behavior, and passage length cap, but it omits the return format, failure modes, and whether the language parameter overrides the passage language.

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 front-loaded with the action and resource, then adds use cases and a key constraint. It is appropriately short with little wasted wording, though the final passage-length fragment is terse and could be integrated more cleanly.

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?

For a generation tool with three parameters, no annotations, and no output schema, the description is incomplete. It lacks return-value shape, language-parameter behavior, and edge-case guidance, leaving the agent to infer too much about invocation and output.

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 for all three parameters. It explains n and the passage length limit, but it does not explain the language parameter's semantics or accepted values, and its language statement could imply auto-detection rather than an override.

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 states a specific verb and resource: write natural, search-style questions that a passage answers. It adds useful qualifiers like language preservation and count, but it does not distinguish this tool from sibling tools such as detect_language or explain when generation should be chosen over them.

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 names use cases like FAQs, study cards, and search/RAG training data, which implies when the tool is useful. However, it gives no explicit when-not-to-use guidance or alternatives among the sibling tools, so the agent must infer selection criteria.

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