compare_forecast_actual

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

No annotations are provided, so the description carries the full burden. It discloses that the tool calculates forecast accuracy metrics and returns a JSON string, but does not describe side effects, data sources, or whether it is destructive. The behavior is fairly transparent but lacks depth.

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, well-structured with clear sections (Args, Returns, Examples), and front-loaded with the core purpose. Every sentence adds value without waste.

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 simplicity of the tool (single parameter, output schema exists, no nested objects), the description fully covers the purpose, input format, and return type. No gaps remain for effective use.

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 one string parameter 'date' with no description (0% coverage). The description adds meaning by specifying the format ('YYYY-MM-DD') and providing an example, which compensates for the schema gap.

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's action ('Compare') and resource ('forecasted vs actual electricity demand'), and specifies it calculates accuracy metrics like error, MAE, RMSE. This distinguishes it from siblings that focus on other analyses like volatility or generation mix.

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 usage for comparing forecasts to actuals and provides a concrete example, but does not explicitly state when to use this tool over alternatives or any prerequisites. No exclusions are mentioned.

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