get_renewable_energy

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

Annotations already declare readOnlyHint=true for safety. The description adds behavioral details: returns capacity totals, fuel breakdown, capacity factor, top projects, and state RPS target. Also explains optional lat/lon for nearest projects. However, it doesn't cover error handling or missing data scenarios.

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 but comprehensive: front-loads usage context, provides example, explains each parameter, describes return object, and warns about alternatives. Every sentence adds value.

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 no output schema, the description adequately conveys return structure and data source. It covers main use cases but could mention edge cases like missing data or lat/lon tolerance. Overall sufficient for the complexity.

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?

Despite 0% schema description coverage, the description fully explains all 4 parameters: energy_type (with options), state (2-letter code), lat/lon (optional). Includes an example call. No parameter ambiguity.

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 retrieves renewable energy capacity and feasibility data for a US state, with specific use cases like siting data centers or PPA sizing. It distinguishes from sibling tools by explicitly naming alternatives for other contexts.

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?

Explicitly states when to use (siting PPA, RE100 feasibility) and when not to use (live grid generation -> get_grid_data, non-US -> get_grid_scoreboard). Provides clear decision rules.

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