ml_forecast_incidents

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

With readOnlyHint=true, the description only needs to add context beyond the safety annotation. It adds 'based on historical trends', which is useful but does not disclose details like response variability, data prerequisites, or internal model dependencies. The description does not contradict 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 a single sentence with no wasted words. It is front-loaded with the primary action and resource, making it easy to scan.

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 lack of an output schema, the description does not hint at the return format (e.g., a time series or single number). It also omits information about the source of historical trends or any assumptions. While the tool is simple and annotations cover safety, this feels slightly under-specified for a forecasting tool.

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 100% since each parameter (category, priority, days_ahead) has a description. The tool description's 'next N days' aligns with days_ahead but adds no additional meaning beyond what the schema already provides, so the baseline of 3 applies.

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 verb ('Forecast'), the resource ('incident volume'), and the temporal scope ('next N days'). It distinguishes itself from sibling ML tools like ml_predict_change_risk and ml_detect_anomalies by focusing specifically on incident volume forecasting.

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 usage for forecasting incident volume, but it does not provide explicit when-to-use vs alternatives guidance. There are no exclusions or named alternative tools, making the usage context only implied.

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