get_model_stats

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

With no annotations, the description carries the full burden. It states the tool returns stats but does not disclose behavioral traits such as whether data is live or cached, any authentication requirements, or performance implications. For a read-only tool with no parameters, the absence of such detail is acceptable but not ideal.

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 short (two sentences) and front-loaded with the core purpose. However, the 'Returns:' line is redundant with the first sentence and could be merged for greater conciseness.

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's simplicity (no parameters, clear output), the description adequately lists the returned metrics. An output schema exists, so deeper details are delegated there. Still, a brief mention of the output schema would improve completeness.

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, and the input schema is fully covered (100%). The description adds no parameter semantics, but the baseline for zero parameters is 4. No further information is needed.

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 retrieves model performance statistics, including win rates by confidence level, total picks, and discipline. It is specific about the resource and metrics, but does not differentiate from the sibling tool get_win_rate, which likely provides a simpler overall win rate.

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 provides no guidance on when to use this tool versus alternatives like get_win_rate or get_completed_picks. It lacks any context for appropriate usage scenarios or exclusion criteria.

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