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

No annotations are provided, so the description carries the full behavioral burden. It does disclose the return contents (min/max/average) and the fact that it is a look-back query, but says nothing about data granularity, freshness/lag, rate limits, or whether an empty window returns an error.

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?

Two sentences, zero filler. The capability statement is front-loaded and the use cases follow in one compact clause.

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, the description usefully names the aggregate values returned and frames the query as a baseline comparison. Missing only operational details such as update cadence and the resolution of the returned series.

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% and the single 'range' parameter is fully documented with an enum and default in the schema. The description's phrase 'over a chosen window' adds only light framing beyond that; baseline 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?

States a specific verb and resource ('Get recent historical Ethereum mainnet gas prices') plus the aggregate returned (min/max/average). The word 'historical' and 'mainnet' implicitly separates it from get_current_gas and get_l2_gas, but no sibling is named 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?

Gives concrete usage contexts — 'what has gas been doing', 'was gas cheaper yesterday', 'compare the current price against a recent baseline' — which maps well to the agent's likely questions. It stops short of naming alternatives (e.g. use get_current_gas for the live price) or stating exclusions.

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