set_qna_phrasing

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

With no annotations, the description carries the burden of disclosing behavior. It includes a useful FLAG that the phrasing schema is large, stored verbatim, and not validated, which warns about input handling. However, it does not disclose side effects like overwriting existing phrasing, permission requirements, or error behavior, so transparency is partial.

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 relatively concise and includes an example plus a warning, but it is a bit loosely structured with a run-on sentence. It earns its place by explaining purpose, parameter types, and a key caveat, but could be tightened for readability.

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 tool with no output schema and no annotations, the description covers purpose, key parameters, and a validation warning, which is useful. However, it does not explain how to construct a valid phrasingJson or what the result/response looks like, and the lack of sibling comparisons reduces completeness for an AI agent deciding among many set_* tools.

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 coverage is 80% (4 of 5 parameters described), which meets the baseline of 3. The description repeats the phrasingType enum and phrasingJson meaning that is already in the schema, adding little new semantics. The FLAG about validation is more behavioral than parameter-focused, so no higher score.

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's function with a specific verb ('Author') and resource ('LSDL Q&A phrasing'), and clarifies it writes to the model's linguistic metadata. The example ('happy customers') makes the purpose concrete. This distinguishes it from siblings like set_synonyms by targeting the Q&A/linguistic context.

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 implies usage through the example of teaching Q&A a concept, but does not explicitly state when to use this tool versus alternatives such as set_synonyms or set_translation. There are no exclusions or direct comparisons to other linguistic metadata tools.

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