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

Beyond the readOnlyHint annotation, the description adds meaningful behavioral context: it discloses a 1-hour cache, the cost per call ($0.05 USDC), payment methods (x402 on Base/Polygon/Solana), and a documentation link. This is valuable transparency about operational aspects, though it does not cover rate limits or error behavior.

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 core description is efficiently written in three sentences, front-loading the purpose and outputs. The additional pricing/payment block is somewhat promotional but includes essential operational information, so it earns its place. Slight overuse of superlatives like 'First-in-world' and 'AI-powered' adds minor fluff.

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 zero parameters and an output schema, the description is contextually complete. It covers the tool's purpose, data sources, output structure, cache behavior, cost, and payment method. The documentation link further supplements implementation details, making this a well-rounded description for an AI agent.

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 input schema has zero parameters, so the baseline is 4 per the rubric. The description does not need to explain parameters, and any potential semantic meaning is irrelevant since no parameters exist. The description adds no parameter info, but that is fine given the schema's completeness.

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 clearly states the tool's function: Korean crypto market sentiment analysis in English, combining exchange intelligence and Korean news context. It also enumerates specific outputs (sentiment label, score, English report, exchange signals, news context), making its purpose unmistakable and distinguishing it from sibling tools that focus on raw metrics like kimchi premium or price data.

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 implies usage context by detailing the combined data sources (exchange intelligence + Korean news) and positioning it as a unique sentiment API, but it does not explicitly specify when to use this tool over alternatives or provide exclusions. There is no mention of sibling tools or conditions that would favor another option.

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