get_ai_usage

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

No annotations were provided, so the description fully discloses behavior: it returns zero values if no AI calls, never throws, and details the error case for missing API key. The return structure is fully outlined, leaving no ambiguity.

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?

The description is well-structured with sections for prerequisites, returns, error behavior, and use cases. It is front-loaded with the purpose. However, it is slightly verbose, though every sentence adds value.

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?

Given zero parameters and no output schema, the description fully covers inputs, outputs, and behavior. It is complete for a simple read tool, leaving no gaps.

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?

There are zero parameters and schema coverage is 100%. The description does not need to add parameter details. Baseline 4 is appropriate as no additional meaning is required.

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 'Get AI token usage and estimated cost for the current MCP server session.' This is a specific verb+resource pair, and it distinguishes from sibling tools like 'get_tokens' which likely refer to design tokens.

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 explicitly lists use cases: monitoring spend, estimating costs, auditing heavy consumers. It also mentions prerequisites and error behavior. However, it does not provide explicit when-not-to-use guidance, though the use cases imply appropriate contexts.

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