Forecast Metric

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

No annotations are provided, so the description carries the behavioral disclosure burden. It adds useful context by mentioning Monte Carlo simulation and confidence-interval bands, but it does not disclose constraints such as required metric existence, period semantics, or what happens with invalid inputs.

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 filler. It front-loads the primary action and packs in the horizon, resource, and method without wasting 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?

An output schema is present, so return-value details are not the description's responsibility. However, the description leaves `confidence_level` semantics, period-unit meaning, and usage context unstated, making it minimally adequate but not fully complete.

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 0%, so the description must compensate. It adds some meaning to `periods` ('periods ahead') and `metric_name` ('named metric'), but `confidence_level` is never explicitly connected to the CI bands, and no value constraints or format details are provided.

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 specifies a clear verb ('Forecast'), a resource ('named metric'), and a distinctive method ('Monte Carlo CI bands'). It also communicates the forecast horizon via `periods`, which makes it easy to distinguish from siblings like query_kpis and detect_kpi_anomalies.

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?

There is no guidance about when to use this tool versus siblings, and no explicit 'use this when...' or 'consider alternatives when...' Exclusions and routing information are entirely absent.

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