forecast_metric

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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds only that it analyzes historical data, but omits important behavioral details such as the external watsonx.ai dependency for the 'ttm' method and the fact that 'auto' selection depends on data and watsonx availability. These behaviors go beyond the annotation hints and should have been disclosed in the description.

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 short and front-loaded with the main purpose. However, the phrase 'using linear regression' is inaccurate given the multiple methods in the schema, which detracts from precision. Still, it is concise and the two sentences are focused.

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?

With no output schema, the description should clarify the return format, but it only vaguely mentions 'trend direction and estimated breach time'. It does not address required parameters, the external dependency implications, or what an agent should expect in the response. The description is insufficiently complete for a tool with 7 parameters and multiple methods.

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 itself does not add meaning to any parameters beyond what the schema already provides, so it remains at baseline without additional credit.

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 states a specific verb, resource, and outcome: forecast when a metric will breach a threshold, predicting trend direction and breach time. However, it claims 'using linear regression' while the schema supports auto, holt_winters, and ttm methods, making the purpose statement incomplete and slightly misleading. It also does not explicitly distinguish itself from sibling analytics tools like detect_anomalies or classify_trend, though the forecasting intent is clear.

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. There is no mention of prerequisites (e.g., WATSONX_API_KEY for ttm), exclusions, or context for choosing forecasting over anomaly detection or trend classification. With many sibling analytics tools present, the lack of usage routing is a significant gap.

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