get_cheapest_hours

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

No annotations are provided, so the description carries full burden. It only describes the tool's function without disclosing any behavioral traits (e.g., data source, latency, error handling, or whether it's a read operation). A read operation is implied but not confirmed.

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 sentences, front-loading the purpose. It is efficient and avoids excess text, though it could be slightly expanded for completeness.

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 explains what the tool does but not the output format or how results are returned. Given no output schema, this is a gap. However, the tool's simplicity and clear parameters partially compensate.

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 coverage is 100%, so the schema already documents all parameters. The description does not add any parameter-specific meaning beyond what the schema provides, maintaining a baseline of 3.

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 finds the N cheapest hours of the day for a price zone, with concrete use cases like scheduling EV charging. It distinguishes from siblings (e.g., get_current_price, get_price_forecast) by focusing on cheapest hours.

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?

Provides clear context on when to use (scheduling appliances) and includes a natural language query example in Danish. Does not explicitly exclude cases or mention alternatives, but the context is sufficient.

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