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

Though there are no annotations, the description clearly discloses critical operational behavior, namely the cost ('0.005 USDC per call') and the specific payment mechanism ('via x402'). This is crucial behavioral context.

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 definition is highly concise, front-loading the core utility in the first sentence. Every part of the description (cost, payment method, utility) is necessary for a complete understanding.

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?

Despite lacking an output schema, the description is complete by detailing the specific use case, the workflow for new users (payment), and the inherent cost of using the service.

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?

Since schema coverage is 100%, the baseline is 3. The description adds value by explaining the complex, multi-step workflow required for the paymentProof parameter (omit on first call, pay, then call again with proof), which is beyond standard schema documentation.

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 tool's purpose is immediately clear: it finds 'Latest token launches that just published a profile.' This distinction helps an agent differentiate it from other broader discovery tools like trending_tokens.

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?

It provides explicit guidance on when to use the tool ('to spot fresh launches early') and suggests a workflow of pairing it with the 'token-audit' sibling tool, which is highly actionable context.

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