forecast_llm_costs

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

The description discloses the internal algorithm (Holt-Winters with fallbacks) and the data source (daily token-cost series). However, it does not state whether the tool is read-only, requires permissions, or has side effects. No annotations are provided, so the description carries full burden but remains incomplete.

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 well-structured with a purpose paragraph, algorithm details, args section, and examples. It is front-loaded with key information and contains no extraneous content.

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?

The description covers the tool's purpose, inputs, outputs, and algorithm, which is comprehensive for a forecasting tool. It lacks information about error conditions, data availability requirements, or output format, but still provides adequate context given no output schema.

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 description explains both parameters (horizon_days and balance_usd) with defaults and examples, compensating for the 0% schema description coverage. It adds meaning beyond the basic schema by clarifying their roles in the forecast.

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 AI/LLM token spend and can calculate exhaustion date for credits. It explains headline outputs (next-30-day spend, growth, runway) and differentiates from sibling tools like forecast_costs by focusing on LLM-specific metrics.

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 three concrete examples that illustrate typical use cases. It implies usage for forecasting rather than current spend, but does not explicitly contrast with siblings like get_llm_costs or specify when not to use.

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