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

Adds substantial context beyond the readOnly/idempotent/openWorld annotations: the x402 paid model ($0.009), the 402-challenge-on-first-call flow, that failed calls are not charged, and crucially that 'boost' is paid promotion not a quality signal and new tokens are high-risk.

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?

Dense but well-organized, front-loading price and what the tool returns before filters, sorting, payment mechanics, and disclaimers. Long, yet each sentence (payment flow, risk warning) is functional for a paid data tool.

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 an output schema present, return values need not be restated. The description nonetheless covers the full picture for a 6-param paid tool: filters, defaults, comparison options, payment protocol, and safety caveats.

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 coverage is 100%, so the schema already documents enum members and defaults (baseline 3). The description elevates this by explaining the semantic meaning of the 'boosted' list (paid promotion) and restating the liquidity-floor default, giving interpretable meaning to the values.

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?

States a precise verb and resource: the union of DexScreener's new/boosted lists with the exact metrics returned (price, liquidity, volume, buys/sells, age). It is clearly distinguishable from siblings like token_price or token_safety, which it names explicitly.

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?

Gives concrete filters, defaults, and routing guidance ('run token_safety before trading'), which tells the agent where this fits in a workflow. It stops short of explicitly stating when NOT to use it versus token_price/token_report, but the context is clear.

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