US Retail Electricity

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

Annotations already indicate read-only, idempotent behavior. The description adds value by stating the data is from EIA public domain with a rate limit of 5K req/hr, and mentions frequency options. No contradiction with annotations.

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, front-loading the key data types and structure. It could be better structured (e.g., separating sentences), but every sentence contributes useful information.

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 5 optional parameters and existence of an output schema, the description covers the main aspects: data includes sales/price/revenue, by state and sector, with multiple frequencies, and source/rate limit. The output schema fills any return format gaps.

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 input schema covers all parameters with descriptions (100% coverage). The description summarizes the main dimensions (state, frequency, sectors) but does not add significant meaning beyond the schema. Baseline score of 3 is appropriate.

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 returns retail electricity sales, price, and revenue data by US state and sector, with specified frequencies. The title 'US Retail Electricity' and sibling tools like finance.eia.natural_gas distinguish it from other EIA data tools.

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 implies this tool is for US retail electricity data, but it does not explicitly mention when to use it over alternatives or provide exclusions. Siblings like finance.eia.natural_gas exist, but no guidance is given.

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