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

Annotations already establish the safe-read profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint=false), so the bar is lower, yet the description still adds meaningful behavior: it discloses that scoring is a deterministic, transparent rubric rather than an AI opinion, lists the exact rubric dimensions, and specifies the returned artifacts including an OG-image share URL. No auth or rate-limit caveats are given, but none appear relevant for a pure-compute roast.

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?

Front-loads the input instruction, then the output contract, then the rubric, then the disclaimer, so an agent can stop reading early if it only needs the input format. The second sentence is dense with comma-separated output items but every clause carries distinct information; nothing is redundant padding.

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?

There is no output schema, so the description must carry the return-value burden, and it does: judges, scores, roast lines, verdict options, the one change, issues/strengths, and share URL are all enumerated. Combined with full schema coverage and the annotation safety profile, an agent has everything needed to call and interpret this tool.

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% and all three parameters (pitch, stage, category) are documented at the schema level, including the stage difficulty ramp and the crypto/blockchain softening. The description's rubric list references terms like 'blockchain' and 'X for Y' but adds no parameter-level format or syntax detail beyond the schema, so the baseline 3 is appropriate.

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?

Names a specific verb and resource ('roast a startup pitch') and enumerates the exact deliverable: five named judges, 1-10 scores, roast lines, a verdict from a fixed set, the single highest-leverage change, and a share URL. It is readily distinguishable from siblings like score_business_idea_moat or ai_bottleneck_quiz.

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 tells the agent what to paste ('one to five sentences') and scopes expectations with 'For fun and practice, not investment advice', which is an implicit when-to-use signal. However, it never names an alternative sibling (e.g. score_business_idea_moat) or states when this tool should not be chosen over those, so usage guidance remains implied rather than explicit.

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