pin_model_digest

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

No annotations are provided, so description carries full burden. It includes '[WRITE][risk=medium]' indicating a write operation with medium risk. However, it does not explain what pinning entails (e.g., reversibility, effect on existing pins), leaving behavioral gaps.

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?

Description is extremely concise, consisting of a single sentence plus a parameter list. The '[WRITE][risk=medium]' tag front-loads key information. No fluff.

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?

Given 2 required params and no output schema, the description explains the purpose and parameter sources but omits details on return values, error states, or prerequisites (e.g., model must exist). Adequate but not comprehensive.

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?

With 0% schema coverage, description adds meaning: it labels the 'digest' parameter as 'the expected digest to pin (from list_models / model_details)', providing source context. The 'model' parameter is only described as 'Model name', which is minimal but acceptable.

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?

Description clearly states the action ('Pin'), resource ('model's expected provenance digest'), and purpose ('drift detection'). It distinguishes from sibling tools like list_models and model_details.

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 context by referencing where the digest comes from (list_models / model_details), but does not explicitly state when to use or not use this tool, nor mention alternatives.

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