grade

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

No annotations are provided, so the description carries the full burden. It discloses the output format (grade, marginalia, summary) and cost but omits behavioral traits like data persistence, idempotency, or potential side effects.

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 two sentences, front-loading the core function (grade with marginalia and summary) and then adding usage and cost. Every sentence provides necessary information without waste.

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 simple tool with one parameter and no output schema, the description adequately explains the output and input constraints (6000 chars via schema). It covers the essential aspects for an agent to decide if and when to use it.

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% with one required parameter 'target'. The description adds value beyond the schema by calling the input 'universal' and listing examples (code, pitch deck, etc.), which helps the agent understand acceptable inputs.

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 specifies the tool grades by providing a letter grade (A+ to F) with marginalia and a summary, and clarifies the universal input type. It distinguishes from siblings like 'roast' by focusing on academic-style grading with specific output components.

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 cases (sharp feedback, prompt engineering eval, pre-investment screen) and cost ($0.01 USDC). While it doesn't mention when not to use or name alternatives, 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.