model_stats

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

No annotations are provided, so the description must carry the behavioral burden. 'Return' implies a read operation, but the description does not disclose side-effect freedom, aggregation behavior, filtering semantics, or any operational caveats.

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 front-loaded sentence with no wasted words. It communicates the core action and resource efficiently.

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 absence of annotations, 0% parameter coverage, and no usage guidance, the description is too sparse. The output schema exists, but the description does not clarify filter semantics, what statistics are included, or whether the call is safe and read-only.

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%, and the description never mentions model_id or work_class. The schema only provides types and defaults, leaving the agent without any explanation of how these optional parameters affect the returned statistics.

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 states a specific verb ('Return') and a precise resource ('empirical cost/verified-outcome statistics from MangoMe execution receipts'). This clearly distinguishes it from action-oriented siblings like record_execution_receipt or verify_slice.

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?

There is no guidance on when to use model_stats instead of status, read_context, or other query-like tools. No alternatives, exclusions, or conditions are mentioned, so the agent must infer appropriateness from the name alone.

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