Get Reading Questions

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

Annotations already mark readOnlyHint=true, so safety is covered. The description adds what the response contains (passage text, question, answer options, correct index) and that filtering by level is optional, which is useful behavioral and output context beyond the schema.

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?

Two short sentences, front-loaded with the main action and output details, followed by a practical use case. No filler or repetition of schema content.

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 simple read-only list tool with two optional, self-explanatory parameters, the description is complete: it states what is returned, the optional filter, and the intended purpose. The output fields are enumerated without requiring an output schema, and annotations cover the safety profile.

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 100%, so the baseline is 3. The description adds little beyond repeating the optional CEFR filter; the limit parameter is only documented in the schema, but the schema already handles that adequately.

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 tool returns reading comprehension questions and can filter them by CEFR level, and it names the key output fields. It does not explicitly distinguish itself from siblings like cl_get_suggested_questions, but the 'auditing' framing adds specificity beyond the title.

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 a clear use case: auditing question quality and level coverage. It does not mention alternative tools or when not to use it, but the auditing context gives enough guidance for typical selection.

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