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

With no annotations, the description carries the transparency burden and does well: it discloses the computed outputs, the proprietary continuous archive, and the $0.12 USDC/x402 cost. It stops short of stating response format or failure behavior, but for a read-only statistical query this is adequate.

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?

Five short, purposeful units: purpose, output list, use case, param sketch, pricing. There is no filler, and the most decision-relevant information is front-loaded.

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?

There is no output schema, so the description compensates by listing the fields returned and the cost to call. However, it leaves the exact response shape and the boundary with preview_kimchi_premium implicit, which is a minor gap.

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 the schema already documents symbol, window, and basis with enums and defaults. The compact param string in the description mostly duplicates that information rather than adding new semantic meaning.

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 names a specific analytical object—where the current kimchi premium falls within its own distribution—and enumerates concrete outputs: percentile, mean, stdev, z-score, min/max/median, and a plain-language read. This clearly distinguishes it from sibling tools like kimchi_premium (current value) and kimchi_premium_history (raw history).

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 an explicit trigger question ('is this premium unusual right now?') and a rationale for using this paid archive over free sources. It does not name siblings to avoid, but the use case is specific enough to route an agent.

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