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

Annotations already cover the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), and the description adds useful context beyond them: data source (EIA), cadence (weekly), and retail market segment. It does not disclose units (¢/gal), the effective time window when start/end are omitted, or response shape, but for a read-only query tool with full annotation coverage this is acceptable value-add rather than a serious 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?

Three short sentences, each earning its place: the first defines the resource and source, the second summarizes the key filters, the third states practical use cases. The core function is front-loaded and there is no filler, tautology, or restatement of the tool name.

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 simple read-only query tool with five optional, fully documented parameters, the description covers source, frequency, filter dimensions, and use cases, and the schema covers all parameters. With no output schema present, the agent is left without return-format expectations, and units or default time-range behavior are unstated — not critical to invoking the tool, but enough to prevent a higher score.

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% — every parameter already carries defaults, allowed values, and examples (e.g., region's 'national'/'PADD1' examples, grade's enum list, limit's 50/5000 bounds). The description's mention of PADD1-PADD5 and the five grade values largely duplicates schema content rather than adding new semantics, so the baseline of 3 applies.

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 the resource precisely — 'Weekly US retail gasoline prices from EIA' — and names the two filter dimensions (region PADD1-PADD5/national, grade with five values). The retail-gasoline focus distinguishes it from adjacent energy siblings like eia_oil_supply and eia_natural_gas, though no sibling is named explicitly. Clear and specific enough for an agent to grasp scope at a glance.

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?

Provides three concrete use contexts — fuel-cost analysis, transportation logistics, and consumer price tracking — which signal when an agent would plausibly need this data. However, it gives no exclusionary guidance or named alternatives despite a large suite of overlapping energy/price tools (eia_oil_supply, fred_observations, energy_market_snapshot), leaving routing decisions to inference.

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