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

Annotations already declare the full safety profile (readOnly, idempotent, non-destructive, closed-world), so the description's added value is the payoff of the response: the policy content plus an 'official policy link'. That return-content hint is useful given there is no output schema, though it stops short of noting behavior when a venue has no published policy.

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?

Two sentences, zero waste: what it returns first, when to use it second. Front-loaded and appropriately sized for a one-parameter read 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?

For a simple, fully-annotated read tool, the description covers purpose, trigger, and response shape. The one unaddressed case is what happens when a venue has no LLM policy on record, which an agent might want to know before relying on the result.

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% and the single 'venue' parameter is documented in the schema as accepting a slug, name or alias. The description adds no formatting or lookup guidance beyond that, so the baseline 3 applies.

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 specific resource and scope: a venue's LLM/AI-use policy, covering both authors and reviewers, plus the official policy link. No sibling in the list overlaps with this domain, so differentiation isn't needed. The only minor shortfall is that the verb ('get') is implicit rather than stated.

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 a clear triggering condition: 'Use before advising someone on AI use for a submission or a review.' There are no exclusions or named alternatives, but none of the siblings compete for this task, so the guidance is sufficient context without being explicit about when not to use it.

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