kopern_get_usage

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

Annotations already declare readOnlyHint true. The description adds behavioral details beyond annotations by listing the specific metrics shown (e.g., grading runs, per-agent breakdown) and explicitly stating 'No LLM cost.' This provides useful transparency without contradicting annotations.

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?

Two concise sentences: the first states the purpose, the second lists the contents. No unnecessary words, and each sentence earns its place.

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 simple retrieval tool with two optional parameters and no output schema, the description adequately explains what data is returned. It covers tokens, cost, grading runs, and per-agent breakdown, which is sufficient for the use case.

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 coverage is 100% with descriptions for both parameters (year_month, include_history). The description does not add further parameter semantics beyond what the schema provides, so baseline score of 3 is appropriate.

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 token usage and cost metrics, specifying what it shows (input/output tokens, cost, grading runs, per-agent breakdown). It is distinct from sibling tools, which cover actions like creating agents, running pipelines, etc.

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 clear context on what the tool does—retrieving usage and cost data—but does not explicitly state when to use it versus alternatives or any exclusions. The context is sufficient given the sibling tools are largely unrelated.

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