metering__record_usage

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

With no annotations provided, the description carries the full burden. It discloses idempotency on event_id and the role of outcome in SLA reports, which are important behavioral traits. However, it does not mention response format, potential errors, or permissions, leaving gaps in transparency.

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 concise with three sentences that front-load domain context and essential details. Every sentence adds value without redundancy, making it efficient for an AI agent to parse quickly.

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 the tool has 5 parameters, no annotations, and no output schema, the description covers the core purpose and key behavioral notes (idempotency, outcome purpose). However, it omits details like return values, prerequisites (e.g., meter must exist), and parameter meanings for meter_id and quantity, leaving the description partially incomplete.

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%, so the description must compensate. It adds meaning to 'event_id' (idempotency key) and 'outcome' (values 'ok' or 'error'), but does not explain 'meter_id', 'quantity', or 'metadata'. Key parameters are left undocumented, which hinders correct invocation.

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 explicitly states 'Record a usage event' with a specific verb and resource. It provides context about SLA accounting and distinguishes itself from sibling tools like 'usage_summary' or 'sla_report' by the action of recording events. The mention of idempotency and outcome adds precision.

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 implies when to use the tool (to record a usage event) but does not explicitly discuss when not to use it or provide alternatives. No exclusions or prerequisites are mentioned, leaving some ambiguity for an AI agent.

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