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

With no annotations, the description carries the full burden and does so admirably. It discloses the specific metrics measured (buy-rate acceleration, buy pressure, volume/price velocity, holder growth, liquidity trend), the classification categories, and the additional risk flags (already-ran tokens and liquidity pulls). This goes far beyond a generic 'scan' and gives agents a clear model of 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 description is about 50 words across two sentences. It front-loads the core purpose as a question, then efficiently lists the measured factors and classification output. Every sentence earns its place with no redundancy or 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?

For a complex analytical tool with four parameters and no output schema, the description provides a solid understanding of what it does and what it returns (score and reasoning, classification). It could slightly improve by explicitly stating return structure (e.g., list of tokens), but the classification and score mention covers the essential output. Given the complexity, this is quite complete.

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% with clear parameter descriptions. The description adds some context by relating 'fresh' to token age and mentioning liquidity trend, but it doesn't provide additional syntax or format details beyond the schema. Baseline 3 is appropriate since the schema already handles parameter semantics effectively.

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 what the tool does: identifies fresh Solana memecoins that are accelerating. It specifies the resource (fresh Solana memecoins), the action (measuring acceleration and classifying), and provides a specific classification scheme (RUNNING/IGNITING/PARABOLIC_LATE/FADING). This distinguishes it well from siblings like new_tokens or token_trend, which focus on other aspects.

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 a clear use case: finding memecoins with accelerating momentum. It does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions. However, the specific metrics and classification make it evident that it's for momentum scanning, which provides clear context. Missing explicit alternatives keeps it from a 5.

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