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

With no annotations, the description carries the full burden. It usefully discloses that the tool returns a 0-100 wash score and costs 0.005 USDC per call via x402, but it does not state whether the operation is read-only, how the score should be interpreted, or what data sources and limitations apply.

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 short, front-loaded with the detection purpose, and then gives return value and payment cost. Every sentence contributes useful information with no wasted text.

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?

The input schema is rich and fully described, so parameter completeness is covered. However, there is no output schema and the description only says it returns a 0-100 wash score without explaining direction, thresholds, or interpretation, leaving a meaningful gap for a scoring 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?

Schema description coverage is 100%, so chain, address, and paymentProof are already well documented in the schema. The description adds no parameter-level meaning beyond mentioning the x402 payment cost, making the baseline 3 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?

The description states a specific verb and resource: it flags wash-trading patterns such as turnover, churn, spam, and thin liquidity, and returns a wash score. It is clear enough to distinguish the tool from general token-market siblings, though it does not explicitly contrast itself with them.

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 explains what the tool detects but gives no explicit guidance on when to use it versus alternatives like token_audit, token_market, or dex_search. Usage is implied by the purpose, but there are no exclusions, prerequisites, or decision criteria.

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