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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds only the vague phrase 'with proof', without saying what the proof is, how it is formatted, or whether the randomness is verifiable against a public beacon. The '[inference]' prefix is unexplained and adds ambiguity rather than clarity.

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?

At eight words it is certainly short and front-loaded, but it is terse to the point of under-specification. The bracketed '[inference]' tag consumes space without conveying usable meaning.

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 carries the full burden of explaining the return value — and 'with proof' is not explained at all (format, verifiability, size). For a tool whose entire value proposition is a verifiable random value, this leaves the agent guessing about what it will receive.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. No argument syntax is needed for a no-argument call.

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 noun phrase 'Quantum random number with proof' identifies the resource but supplies no verb, so it is unclear whether the tool generates a new number, fetches one, or returns a proof artifact. Nothing distinguishes it from the sibling rng_verify, which likely consumes the output of this tool.

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?

There is no indication of when to call this versus rng_verify, nor any prerequisite, cost, or latency context. The agent must infer the relationship between the two rng tools entirely on its own.

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