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

Annotations already mark readOnlyHint=true, and the description adds valuable context about the score's composition (price-per-kWh history since 1978, percentile normalization, permit friction driver), the output fields, and monthly updates. This goes beyond annotations without contradicting them.

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 two sentences, with the first packing all necessary output and methodology details and the second giving usage context and update frequency. There is zero fluff; every clause earns its place.

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?

Although there is no output schema, the description enumerates all return fields, explains the score's meaning, provides use cases, and notes the monthly update. The optional `days` behavior is covered by the schema, so nothing critical is missing for an agent to decide whether to invoke this tool.

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 only parameter, `days`, has 100% schema coverage with a thorough description including Gold-tier requirements and fallback behavior. The main description does not add any extra parameter meaning, so the baseline 3 applies exactly.

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 uses a specific verb ('Returns') and names the exact resource ('US residential electricity cost-barrier score') with a 0-100 scale and a list of output fields. It clearly distinguishes this tool from its many siblings with similar opaque names.

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 explicitly states when to call the tool: when the user asks about household electricity costs, electrification headwinds, or EV-adoption barriers, or when timing charger deployment and EV market-entry decisions. It does not name alternative tools or exclusion criteria, so it falls just 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.