get_ai_usage

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

No annotations provided, so description carries full burden. It discloses in-memory reading, returns zero values if no calls, never throws, and specifies the return object fields including error summary when API key missing. This fully informs the agent of behavior.

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?

Concise structure: first sentence states purpose, then prerequisites, return format, error behavior, and use cases. Every sentence adds value with no redundancy.

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?

Despite no output schema, description provides complete return structure and error handling. For a zero-parameter tool, it fully equips the agent with necessary context.

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?

Input schema has no parameters (100% coverage baseline 4). Description adds no parameter info because none exist, but this is appropriate given zero parameters.

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 starts with 'Get AI token usage and estimated cost for the current MCP server session', clearly stating the verb and resource. It distinguishes itself from sibling tools by focusing on monitoring AI usage, which is unique among listed siblings.

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 states 'Prerequisites: None' and provides concrete use cases: 'to monitor token spend during a session... to estimate costs... or to audit which tools are the heaviest AI consumers'. Also explains error behavior, guiding appropriate use.

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