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

Annotations already declare readOnly, idempotent, and closed-world, so the safety profile is covered; the description adds substantial context beyond that by listing every output metric and citing the underlying datasets (EIA retail sales, Form 861, RECS). The 'not a bill audit' caveat usefully sets accuracy expectations. It does not state how missing optional inputs change the response, which is the remaining gap.

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?

Front-loaded with the core comparison and its dimensions, followed by the return-value list, data provenance, and a scope caveat. Dense but each clause carries information; the dataset enumeration is useful for trust but is the least essential element.

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?

With no output schema, the description correctly compensates by enumerating the returned fields (percentile, typical kWh range, rate vs. average, dollar gap, HVAC share, likely causes). Combined with the stated inputs, data sources, and accuracy caveat, an agent has everything needed to decide and call correctly.

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 100%, so every parameter's meaning and format (state code or name, 1-12 month range, sqft example, household size bounds) is already documented in the schema. The description adds no syntax or interpretation guidance beyond it, which is the expected baseline when the schema does the work.

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?

States a specific action (compare one household's monthly use and price against similar homes) and enumerates the comparison dimensions (state climate, home size, household size, month) plus the returned metrics. It is unmistakably a comparative-analysis tool rather than a raw lookup, though it never names find_utility_rate or get_state_electricity_stats to draw the boundary explicitly.

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 opening sentence establishes the exact usage context (one U.S. household, one month) and the optional parameters' conditions are noted (month 'recommended' because of seasonality, utility for rate comparison, amount_due to enable it). The closing 'Statistical comparison, not a bill audit' sets a clear when-not boundary, but alternatives among the siblings are never mentioned.

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