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

With no annotations, the description carries the full burden and does well: it discloses data provenance (EIA Form 861), temporal granularity (monthly average, latest), and an explicit accuracy caveat that the value is state-level and not ZIP- or utility-specific. It omits operational traits such as auth requirements, rate limits, or freshness lag, keeping it from a 5.

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?

Three tightly written sentences: purpose and units first, then the accuracy caveat, then the intended downstream use. Every sentence carries information and nothing is padded.

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?

For a single-parameter read tool with no output schema and no annotations, the description covers output units, granularity, provenance, accuracy limits, and use case. An agent has everything needed to call it correctly and interpret the result.

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 baseline is 3, but the description adds real meaning by explaining that the ZIP input is resolved against state-level data rather than ZIP-specific rates — an important caveat an agent cannot infer from the schema alone.

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 states a specific verb (Get) and resource (latest monthly average retail electricity rate), plus units, geographic scope, and sector breakdown. It implicitly separates itself from the sibling utility_tariff by clarifying the data is a state-level ballpark rather than utility- or ZIP-specific tariffs.

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?

It gives clear context for use (state-level approximation, pairs with emission factors for carbon-cost estimation) and implicitly signals that utility_tariff should be used when actual tariffs are needed. However, it never names the alternative or states an explicit when-not-to-use rule, so it falls short of a 5.

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