get_usage_hourly

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

With no annotations, the description carries the full burden, and it does disclose the fixed 24-hour scope, hourly bucketing, and that the metric is delivery volume. It omits what counts as a delivery (attempts vs. successes vs. failures), whether timestamps are UTC, and the return shape — meaningful gaps for a metrics tool with zero annotation coverage.

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 short sentences, front-loaded with the operation and scope, then the use case. Nothing repeats the name or wastes words.

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 zero-parameter read tool with no output schema, the description covers what is returned, over what window, and at what granularity. Only minor details (timezone, success/failure accounting) are missing, which is acceptable at this complexity.

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?

The tool takes no parameters, so the baseline is 4. The description does add value by making clear the time window is fixed at 24 hours, so an agent does not go hunting for an undocumented range argument.

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?

States a specific verb and resource ('Get webhook delivery volume') plus the granularity ('broken down by hour') and the fixed window ('last 24 hours'), which separates it from get_usage, get_usage_history, and get_usage_by_feed in practice. It never names those siblings, so an agent must infer the distinction from the qualifiers rather than being told explicitly.

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?

'Useful for identifying traffic spikes' supplies one implied use case, but there is no explicit statement of when to prefer this over get_usage_history or get_usage_by_feed, and no exclusions. Usage guidance is present but left to inference.

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