forecast_costs

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns a forecast with prediction intervals and automatically tunes parameters, but does not explicitly state whether it is read-only or has side effects. It does not contradict any annotations (none provided), so no contradiction.

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 concise with two paragraphs. The first paragraph front-loads the purpose and method. The second paragraph lists parameters efficiently. Every sentence adds value with no fluff or repetition.

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's complexity (4 parameters, no annotations, no output schema), the description covers the return values (method, MAPE, monthly projection, day-by-day estimates) and important parameter constraints. It does not mention error conditions or account_id format, but is largely complete for an informed 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?

The input schema has 0% description coverage, so the description must compensate. The 'Args' block provides meaningful explanations for all four parameters, including defaults and constraints (e.g., history_days requires ≥14). This adds significant value beyond the schema's bare types and names.

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 forecasts future cloud spend using Holt-Winters time-series modeling. The verb 'Forecast' and resource 'cloud spend' are specific. It distinguishes from siblings like 'forecast_azure_costs' and 'forecast_llm_costs' by focusing on general cloud spend and mentioning the specific method.

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 when to use the tool (for forecasting cloud spend with historical data) but does not explicitly state when not to use it or how it differs from alternatives. It mentions automatic tuning of parameters but lacks explicit guidance on selecting this tool over sibling forecasting tools.

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