get_model_metadata

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

With no annotations provided, the description carries the burden of behavioral disclosure. It correctly identifies the source (safetensors header) and the parameter constraints (sd15/sdxl, checkpoints/loras/vaes). It does not mention side effects, error conditions, or latency expectations. For a read operation this is acceptable, but it doesn't go beyond the schema in explaining behavior.

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 focused and space-efficient. The first sentence states the purpose, followed by parameter documentation. It doesn't waste words. Minor deduction as it could front-load the key fact that this is a read-only metadata/safetensors_header query more explicitly, but overall it's concise and scannable.

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?

There is an output schema (not shown in this evaluation), which reduces the need to describe return values. Given that, the description covers the key context: what metadata source (safetensors header), and the constrained parameter values. It could add expected behavior when a model isn't found or how the response is structured, but the output schema presumably covers that. Effective and complete for the common case.

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. It does: each parameter includes its purpose and allowed values (e.g., model_type: "sd15" or "sdxl"). This is a strong contribution over the bare schema which just has parameter names. All three params are explained with concrete examples.

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 it gets metadata for a model, naming the source (safetensors header) and the resource type. This differentiates it from list_models (which likely lists available models) and other sibling tools. The verb 'get' plus specific resource 'model metadata' is clear enough, though it could be slightly more explicit about being a read-only operation.

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 clearly indicates when to use it: to retrieve metadata for a specific model, as opposed to listing all models (list_models). The usage context is explicit in the parameter descriptions (model_type, resource_type). However, it doesn't explicitly exclude when not to use it or name direct alternatives, though the tool name itself is descriptive enough.

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