tokmeter_efficiency

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

Annotations already establish this as a safe read-only, non-open-world operation, so the description's job is lighter. It adds interpretive context ("high cache rates mean less wasted compute") and scope defaults are carried by the schema, but there is no output schema and the description never characterizes the result shape beyond metric names or what happens with sparse data.

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?

Three short sentences, front-loaded with the metric inventory, then a one-line interpretation, then the action. Nothing is padded or repeated, and the interpretive clause earns its place by telling the agent what a high cache rate implies.

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?

For a read-only aggregate analytics tool with full schema coverage and no output schema, the description covers what is measured and why. What is missing is the boundary against tokmeter_cache_efficiency and any sense of result granularity, which matters in a 23-tool family with heavy conceptual overlap.

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 100%, so the baseline of 3 applies. The description's mention of "models or projects" loosely gestures at the project and providers filters and doesn't explain the scope enum semantics already documented in the schema, so it adds no meaning beyond structured fields.

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?

Names a specific verb (Analyze) and enumerates the concrete metrics it returns: cache hit rates, reasoning token ratios, input/output efficiency, and cost-per-token. An agent knows exactly what data comes back, though nothing here separates it from the sibling tokmeter_cache_efficiency, whose name implies overlapping coverage.

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?

"Use this to optimize costs by identifying models or projects with poor cache utilization" gives one genuine usage context, but there are no exclusions and no named alternatives despite an obvious candidate (tokmeter_cache_efficiency) and related tools like tokmeter_cost_optimization_tips and tokmeter_compare. The guidance is implied rather than decided for the agent.

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