Record a usage event. Idempotent on event_id (safe to retry — never double-billed). outcome is 'ok' or 'error' and feeds the SLA report.
| Name | Required | Description | Default |
|---|---|---|---|
| outcome | No | ok | |
| event_id | Yes | ||
| metadata | No | ||
| meter_id | Yes | ||
| quantity | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses key behavioral traits: idempotency on event_id and that outcome feeds the SLA report. However, it lacks details on destruction, rate limits, or response format.
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?
Two concise sentences, front-loaded with the action. 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?
For a tool with 5 parameters and an output schema, the description covers only idempotency and outcome significance. It fails to explain the purpose of meter_id, quantity, or what the output contains, leaving significant 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?
Schema coverage is 0%, and the description only adds meaning for event_id (idempotent) and outcome (ok/error, feeds SLA report). It omits meter_id, quantity, and metadata, leaving them unexplained.
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 the tool's action ('Record a usage event') and resource, distinguishing it from sibling tools like usage_summary and usage_timeseries by the verb 'record'.
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?
No guidance on when to use this tool versus alternatives or when not to use it. The description only explains what it does without context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct aspect of usage metering: creating meters, recording events, generating reports, flagging synthetic data, closing periods, and verifying chain integrity. No two tools have overlapping purposes.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., close_period, create_meter, list_events). No deviations or mixed conventions.
With 11 tools, the set is well-scoped for a metering server. It covers creation, recording, listing, reporting, admin flags, period closing, and chain verification without being unnecessarily large.
The core lifecycle is covered: create meter, record usage, list events, generate reports, close period. Missing are tools to update/delete meters or retrieve invoices, but these are minor gaps given the immutable design intent.