log_usage

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

No annotations are provided, so the description must disclose behavior. It mentions logging but does not state whether the operation is idempotent, how duplicates are handled, or if there are side effects like rate limits. Lacks critical behavioral context.

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 a single sentence that is front-loaded with the core purpose. Efficient and direct, though it could benefit from brief elaboration on optional fields. Waste is minimal.

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 10 parameters, very low schema description coverage (20%), no output schema, and no annotations, the description is far from complete. It omits behavioral expectations, return values, and explanations for most parameters, leaving significant gaps for the agent.

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 only 20% (2 of 10 parameters described). The tool description adds no parameter details beyond the schema, leaving most parameters (session_id, provider, cache tokens, etc.) unexplained. Fails to compensate for low coverage.

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?

Clearly states the tool logs a single LLM API call with token counts, and distinguishes it from sibling tools that export, hint, or summarize. The verb 'log' and resource 'LLM API call' are specific and unambiguous.

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?

Provides clear when-to-use instruction: 'Call this after every Claude/GPT/Gemini response.' While no explicit when-not-to or alternatives are given, the context from sibling tools makes misuse unlikely.

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