prompt_dialect

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

No annotations are provided, so the description carries the full behavioral disclosure burden and does so thoroughly. It reveals caching by file hash, the ambiguous xl case covering multiple dialects, the need for operator confirmation, and the warning that quality tags meaningfully affect output. No hidden side effects or surprising behaviors are apparent.

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 purpose, then organized into return contents, operational warnings, ambiguity handling, and caching behavior. Every sentence contributes meaningful information without filler or repetition.

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?

With no output schema and no annotations, the description fully covers required return semantics: possible dialects, quality prefix, negative baseline, and provenance. It also explains the ambiguous result path, the confirm parameter, and the caching behavior, making the tool safely and correctly callable by an agent.

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 input schema only exposes an optional string confirm with no description, and schema coverage is 0%. The description compensates by explaining that confirm should be set to the operator's answer when the dialect comes back unknown, tying the parameter to the ambiguity workflow. It does not explicitly enumerate valid confirm values, but the dialect list in the description implies the expected value space.

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 the prompting dialect of the loaded checkpoint, enumerates all dialect values, and names the return components: quality prefix, negative baseline, and source. It is distinct from sibling tools like model_profile because it focuses specifically on prompt expectations and quality tags.

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 gives clear context: use the tool to learn how the loaded checkpoint must be prompted, especially regarding quality tags. It also covers the conditional workflow when the dialect is unknown, telling the agent to ask the operator and call again with confirm. It does not explicitly name sibling alternatives or when not to use it, but the context is strong.

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