log_cost

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

No annotations are provided, so the description carries the full burden. It conveys that the tool logs data without side effects like modifying state or triggering other actions. Could add that it is a record-only operation, but current description is sufficient.

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?

Description is extremely concise: two sentences for purpose and context, then a clean Args list. Every sentence adds value, no fluff, and front-loaded with the main action.

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 logging tool with 4 parameters, the description covers the what, when, and how. Sibling tools provide context for when to use this vs others. An output schema exists but description doesn't need to detail return values.

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%, but the description compensates fully by defining each parameter (model with examples, tokens_in/tokens_out as input/output tokens, task as description). This adds meaning beyond the schema's type definitions.

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?

Description clearly states it logs token usage and cost for a task, specifying to call after each AI interaction. It distinguishes from sibling tools like cost_report (for reports) and estimate_cost (for estimation) by focusing on per-interaction logging.

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?

Explicitly says 'Call this after each AI interaction.' This provides clear when-to-use context. While not listing exclusions, the sibling tools offer natural alternatives for other needs, making usage clear.

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