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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the read-only nature is covered. The description adds the important behavioral constraint that the venue must already be forecasted, which is context beyond the annotations.

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 two sentences plus a succinct example. It front-loads the core purpose, then provides the conditional behavior and a concrete invocation. No filler or redundant phrasing.

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 simple read tool with 4 params and no output schema, the description covers the essential behavior and inputs. The example clarifies key parameters. It could mention the output format explicitly, but the description's statement of 'busyness intensity' and 'whole day's curve' is sufficient for an agent.

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 100%, so each parameter is documented. The description adds meaning by explaining that omitting hour returns the whole day's curve and that venue_id comes from besttime_forecast. This goes beyond the schema, giving the agent actionable guidance on parameter usage.

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 reads busyness for a venue at a specific hour or day, and explicitly ties it to an already-forecasted venue via venue_id from besttime_forecast. This distinguishes it from siblings like besttime_live and besttime_forecast, and the example reinforces the intent.

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?

It explains the prerequisite of having a forecasted venue and how to request a single hour vs. the full day curve. While it does not explicitly name alternative tools, the dependency on besttime_forecast is clear, and the optional hour parameter is well explained.

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