get_generation_mix

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

With no annotations provided, the description carries the full burden and does so excellently. It discloses the ownership default, the existence and risk of non-owned rows, the behavior of include_purchases, case-insensitive partial-name search, and the default year range. It even warns about the overstatement pitfall from mixing purchased supply with owned generation.

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 front-loaded with the core purpose and output metrics, then logically progresses through the ownership default, caveat about non-owned rows, and optional parameters. Every sentence adds necessary operational detail, and the structure makes the information easy to scan.

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 is complete for a data-retrieval tool with an output schema. It covers defaults, edge cases, parameter behavior, and the important conceptual distinction between generation and purchased supply. Nothing needed for correct invocation is missing.

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 description coverage is 0%, but the description compensates by explaining all three parameters: utility_name supports case-insensitive partial matches, year is optional and defaults to all years 2005-2024, and include_purchases is thoroughly described with its effect on row splitting and columns. This adds substantial meaning beyond the bare schema types and defaults.

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 opens with a specific verb and resource: 'Get electricity generation breakdown by technology for a utility.' It also names the output metrics (capacity, net generation, capacity factor, CO2 emissions) and technology groups, making it clearly distinguishable from siblings like get_emissions_trend and get_climate_alignment.

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 gives clear guidance on the default owned-generation scope and explains exactly when to set include_purchases=True. It does not explicitly name alternative tools or state when not to use this tool, but the context it provides is sufficient for most selection decisions.

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