ml_forecast_incidents

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

The annotations already indicate readOnlyHint and openWorldHint. The description adds no behavioral context beyond the purpose, such as how the forecast is generated or that results may vary. No additional transparency is provided.

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, clear sentence that front-loads the verb 'Forecast'. It is efficient but borders on being too terse; however, it wastes no 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?

Given the tool is an ML forecasting function with no output schema, the description lacks details on return format (e.g., single number, time series) or model behavior. The agent may not have sufficient context to use the output correctly.

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%, so the baseline is 3. The description does not elaborate on parameters (category, priority, days_ahead), but the schema already documents them adequately, so no penalty.

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 'Forecast incident volume for the next N days based on historical trends' clearly states the verb (forecast), resource (incident volume), and scope (next N days). It distinguishes itself from sibling ML tools like ml_predict_change_risk or ml_similar_incidents by focusing on volume prediction.

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 provides no guidance on when to use this tool versus alternatives such as ml_predict_change_risk or ml_similar_incidents. It lacks explicit usage context, exclusions, or prerequisite conditions.

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