analyze_model

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

With no annotations, the description carries the behavioral burden and does disclose output ordering: warnings first, each with its fix. However, it does not state whether the tool is read-only, what permissions are needed, or other operational constraints.

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 a single, efficient sentence with the core output ('metrics and suggestions') front-loaded and the warning-first ordering clearly stated. There is no wasted text.

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?

The tool is simple and has an output schema, so return values need not be fully described. Still, the description omits usage routing against siblings and does not clarify the required model-name parameter, leaving meaningful gaps for 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 schema has 0% description coverage and the description never explains the single required 'name' parameter. While 'built model' weakly implies the name identifies the model, the description does not add meaningful semantics beyond the bare schema property.

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 that the tool returns metrics and suggestions for a built model, with warnings first and fixes included. It is more specific than a tautology, but it does not explicitly differentiate this diagnostic tool from siblings like describe_model or doctor.

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 built model' implies the model must already exist, but there is no explicit guidance on when to use this tool versus describe_model, doctor, or check_print. No exclusions or alternatives are named.

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