demand_vs_weather

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

No annotations are provided, so the description bears full responsibility for transparency. It discloses the method ('using the NOAA weather station nearest that region's main load zone'), the output structure (one row per day, average demand MWh, max/min temp C), and the join logic. It does not mention potential limitations like missing data or time zone handling, but the core behavior is well explained.

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 and well-structured: a lead sentence explaining the purpose, followed by a bullet-like Args list. Every sentence adds value, and there is no redundancy. The key information is front-loaded.

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 moderate complexity (3 required parameters, output schema exists), the description is complete. It explains the input format, the join logic, and the output structure (one row per day, demand and temperature). The presence of an output schema means the full return format is already defined, so the description's summary is sufficient.

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 provides clear, actionable semantics for all three parameters. It explains region as 'Balancing authority code', start and end as 'Inclusive start date, YYYY-MM-DD' and 'Inclusive end date, YYYY-MM-DD'. This adds significant value beyond the schema, which only has titles. With 0% schema description coverage, the description fully compensates.

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 purpose: joining daily average demand against daily temperature for a region to reveal the demand-temperature relationship. It uses a specific verb ('join') and resource ('demand vs weather') and distinguishes itself from sibling tools like query_demand, daily_demand, and compare_regions by focusing on the combined analysis.

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 explains the tool's use case (revealing heating and cooling load relationships) but does not explicitly state when to avoid it or name alternatives. However, the context (sibling tools) and the description's wording imply that this tool is for combined demand-weather analysis, which is a distinct purpose among the siblings.

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