grade_or_not

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

The description discloses a key behavioral trait: 'PAID: $0.10 USDC per call.' It does not mention side effects, data modification, or permissions, and since no annotations are provided, the description carries the full burden. The cost is transparent, but the lack of any statement about read-only behavior or side effects leaves some ambiguity, yielding a middling score.

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 extremely concise and well-structured: it leads with the core questions, explains the methodology, states the output, and adds a usage condition. Every sentence adds value and there is no fluff, making it highly efficient for an agent to parse.

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 moderate complexity (4 parameters, no output schema), the description provides sufficient context: it explains the inputs' general role, the calculation approach, and the output format (GO/NO-GO with best/predicted/worst profit). It does not cover error cases or edge conditions, but for a straightforward decision-support tool this is adequate, so it earns a strong but not perfect score.

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 schema provides only parameter names and defaults with no descriptions (0% coverage). The tool description mentions concepts like 'AI grade prediction' and 'PSA fee schedules' that implicitly relate to predicted_grade and service_tier, but it does not explicitly define each parameter or clarify nuances such as the meaning of raw_price or allowed values for service_tier. This falls short of compensating for the absent 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 description clearly states the tool answers 'Should I grade this card?' and 'Will I make money?' by combining AI grade prediction with PSA fees, shipping costs, and market values to return a GO/NO-GO verdict and profit scenarios. This is a specific, verb-driven purpose that distinguishes it from sibling tools like market_snapshot or grade_card.

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 explicitly states 'Use this when: a user is deciding whether to submit a card for professional grading and wants to know if it's financially worth it.' This provides a clear trigger condition, though it does not explicitly contrast with alternatives or state when not to use the tool, so it falls just short of a perfect score.

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