tokledger_model_breakdown

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

No annotations are provided, so the description carries the full behavioral burden. It does not state that the operation is read-only, whether it is scoped to a time window, whether it requires authentication, or how expensive/rate-limited the call is. It only describes the output fields.

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?

A single dense fragment that front-loads the distinguishing scope ('Per-model rollup') before the metric list. It is efficient, though the telegraphic phrasing is slightly terse for a tool whose scope boundaries are unstated.

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?

With no output schema, the description should fully explain what is returned; it lists the metric columns but omits the dimensions (e.g., whether latency/cost are per-request averages, the time range covered, and currency unit for cost). For a zero-parameter reporting tool the essential shape is conveyed, but key scoping details are missing.

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 takes zero parameters, which is the baseline case for a 4 per the rubric. The description correctly implies the tool operates over the whole tracked dataset without any inputs to configure.

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 names a specific resource (per-model) and enumerates the metrics returned (requests, tokens, avg TTFT, local and cloud-equivalent cost), so the agent knows exactly what this tool produces. It does not explicitly contrast itself with siblings like tokledger_stats or tokledger_cloud_savings, though the 'per-model' scope implicitly separates it from a global stats tool.

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 when-to-use guidance, no mention of alternatives, and no stated conditions or prerequisites. The agent must infer from the name alone whether this is preferred over tokledger_stats for model-level questions.

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