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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is covered. The description adds behavioral context by introducing the training/test paradigm and stating the output ('risk score and which metrics are suspicious'). This goes beyond the annotations without conflicting with them.

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 concise and well-structured: two short paragraphs with a clear purpose statement, usage explanation, and a concrete example. Every sentence contributes value, and the example aids comprehension without bloat.

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 the tool's complexity (training/test model) and absence of an output schema, the description provides sufficient context about inputs, expected behavior, and output. It could be more complete by mentioning the sensitivity parameter, but that is documented in the schema.

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 schema already describes each parameter including example fields for training ('price, volume_24h, market_cap, holders, liquidity, age_days, etc.'). The description repeats similar field examples and explains the training/test conceptual split, but adds only marginal detail 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 the tool's purpose with a specific verb ('assess') and resource ('crypto token legitimacy risk'), and further specifies detections ('pump-and-dump patterns, fake metrics, and anomalous token profiles'). It distinguishes itself from siblings like waveguard_price_manipulation by focusing on a training/test approach.

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 how to use the tool with a concrete example ('Pull CoinGecko data for 20 established tokens → train. Test a new token → get risk score'). It implies when to use it (when you have known-good and suspect tokens) but does not explicitly mention alternatives or exclusions, so it doesn't earn a 5.

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