archive_model

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

With no annotations, the description must carry the behavior and safety burden. It discloses that lifecycle state changes, which is useful, but does not say whether archiving is reversible, whether it blocks prediction or comparison workflows, or what side effects occur. 'Update its lifecycle state' is a vague gesture at consequences.

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 nine words, front-loaded with the action and object, and contains no filler. The added 'lifecycle state' clause earns its place by clarifying the broader behavior.

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 state-changing tool with no annotations and no output schema, the description omits lifecycle details, irreversibility, return value, and any prerequisites. It could be called by an agent, but not with confidence about consequences or downstream effects.

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 description mentions no parameters and does not explain model_id semantics beyond the schema. Context reports 0% schema description coverage, so the description does not compensate; though the single parameter name is fairly self-explanatory, there is no guidance on identifiers, format, or required state.

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 clear imperative verb ('Archive') and names the specific resource ('a model record'), then adds that it updates lifecycle state. Among siblings focused on prediction, fitting, and comparison, archive_model is unambiguous and not a tautology.

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?

No guidance is provided about when archiving is appropriate, whether archived models are excluded from other workflows, or when to prefer a sibling like compare_models or get_model_status. The agent must infer usage solely from the verb and tool name.

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