Screening: gerankte Token-Liste (bezahlt, x402)

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

Discloses that the tool is paid (USDC via x402), which is critical behavioral info. However, it does not explain any other behaviors such as caching, rate limits, or whether the list is always fresh. With no annotations, the description carries the full burden but leaves gaps.

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 sentences, front-loaded with the most important information (paid, ranked list). No unnecessary words. Efficient and clear.

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?

Given no output schema, the description adequately lists the fields in the returned list. Parameters are simple. Context for usage as a regular screening tool is provided. Could be more specific about exact structure, but sufficient for the complexity.

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 covers both parameters with descriptions (100% coverage). The description adds value by mentioning the output fields (score, riskLevel, etc.), helping to interpret the effect of minScore. This goes beyond the schema.

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 it returns a ranked list of prescreened tokens with fields like score, riskLevel, action, etc. It specifies it is ideal for screening/trading agents. However, it does not explicitly differentiate from sibling tools like score_token or token_summary, which focus on single tokens rather than lists.

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 mentions it is ideal for regular opportunity scanning agents, giving a usage context. But it lacks explicit guidance on when not to use it or how it compares to alternatives like sample_yields or yield_opportunities.

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