get_model_details

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

With no annotations, the description carries the behavioral disclosure burden; it clearly signals a read-only fetch by using 'Get' and specifies the meaningful output dimensions beyond the output schema. It does not mention error cases or special requirements, but for a simple single-model retrieval there are no major side effects to disclose.

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 main behavior is stated in the first sentence with no filler, followed by a single clearly formatted argument explanation. Every sentence serves a purpose.

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 one-parameter get-details tool, the description covers the required argument, gives an example, and an output schema exists to define the return structure. Nothing critical is missing for successful invocation.

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?

Input schema coverage is 0%, and the description fully compensates by explaining that model_id is the Hugging Face model repo id and providing a concrete example ('meta-llama/Llama-3.1-8B-Instruct'). This is exactly what an agent needs to construct a valid call.

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 opens with a specific action and resource: 'Get full details for a single Hugging Face model,' and enumerates the exact content returned (parameter count, license, tags, pipeline task). This clearly distinguishes it from siblings like search_models or compare_models, which serve different purposes.

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 phrase 'for a single Hugging Face model' and the detail list give clear context for when this tool is appropriate, and it stands apart from the search/compare/hardware siblings. It does not explicitly name alternatives or state when not to use it, so it stops short of full routing guidance.

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