find_greenest_time_window

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

No annotations, but description clearly states it returns the best time window with lowest CO2 intensity in next 48 hours plus comparison to current value. Does not disclose potential accuracy limitations or reliance on forecast data, but provides essential behavioral context.

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?

Concise German description with clear sections: purpose, ideal uses, parameters, returns. No fluff, every sentence adds value. Front-loaded with action and resource.

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?

No output schema, but description covers return value (time window with CO2 comparison). Could specify output format (e.g., timestamp range), but sufficient for effective tool use. Sibling tools contextually differentiate this as a scheduling optimizer.

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 has 0% description coverage, but description explains the sole parameter 'duration_hours' as task duration in hours with default of 2. Adds meaningful context beyond schema type and default value.

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?

Clear verb 'findet' (finds) and resource 'grünste Zeitfenster' (greenest time window) with specific use case for AI tasks in UK. Differentiates from sibling tools that provide current or forecast carbon intensity values rather than optimal scheduling windows.

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?

States ideal use cases: GPU training, batch processing, large inference jobs. However, does not explicitly mention when not to use or contrast with alternatives like get_uk_carbon_intensity (which gives current values) or get_carbon_intensity_forecast (which gives forecast series).

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