get_model_metrics

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

No annotations are provided, so the description carries the full burden. It states it 'returns' counts, implying a read-only operation with no side effects. However, it does not disclose details such as whether counts include nested elements, whether it is scoped to the entire model, or any performance implications. The description is truthful but minimal, leaving some behavioral ambiguity.

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 concise sentence with no wasted words. It front-loads the key action and output, making it easily scannable. It is appropriately sized for a tool with no parameters and a simple 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?

Given the tool has no parameters, no annotations, and an output schema exists, the description is largely sufficient. It explains what the tool returns, and the output schema presumably details the exact structure. The only missing context is whether counts are global or scoped, but for a simple metrics tool this is a minor gap. The description covers the essential information an agent needs to call it correctly.

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 tool has zero parameters, so the baseline is 4. The description adds meaning by specifying what the output is (raw element counts) and the categorization (by UML/SysML type), which clarifies the tool's purpose even without parameters. No parameter documentation is needed, so the description appropriately focuses on output semantics.

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 the tool's function: returning raw element counts broken down by UML/SysML type. It is distinct from siblings like find_by_type or search, as it provides aggregate statistics rather than individual elements. The verb 'return' and specific resource make the purpose unambiguous.

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 does not explicitly mention when to use this tool versus alternatives or provide any exclusions. It implies usage for obtaining model metrics, but lacks guidance on when not to use it or how it relates to similar query tools. Since it has no parameters, the context is somewhat clear, but the lack of any usage direction prevents a higher score.

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