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get_random

Draw unbiased, Ed25519-signed verifiable randomness for fair selections, raffles, and commit-reveal protocols. Ensure third-party verifiability with cryptographic proofs.

Instructions

Draw unbiasable, Ed25519-signed verifiable randomness (Platon).

Use this when you need randomness an autonomous agent cannot bias or predict and that a third
party can verify — fair selection, sampling, raffles, commit-reveal, anti-MEV ordering. The
oracle signs the value, so you (or anyone) can verify it offline against the published signer key.

Returns:
    The standard envelope (see server instructions). `result` contains:
      - `random_hex`: the random bytes, hex-encoded (`num_bytes` long).
      - `proof`: `{state_hash, tick, timestamp, entropy_commitment}` binding the value to the
        oracle's chaotic state at draw time.
      - `signature`: Ed25519 signature over the value+proof (verify with `verify_random`/the
        signer key in the Hub manifest). `verifiable.signed` will be true.
    Cost ~$0.004 USDC, charged per call.

Example:
    get_random(num_bytes=32, client_seed="0xdeadbeef")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
num_bytesNoNumber of random bytes to draw, 1..1024. The result's `random_hex` is this many bytes hex-encoded. Use 32 for a 256-bit seed/word.
client_seedNoOptional caller-supplied seed (hex) for domain separation — it is bound into the signed proof so two callers asking at the same tick get distinct, attributable randomness. Pass '' to omit.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully carries behavioral transparency. It details the return envelope (random_hex, proof, signature), explains that the oracle signs the value for offline verification, and notes the cost (~$0.004 USDC). No missing behavioral traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise and well-structured: single-sentence purpose, usage guidelines, return values in bullet format, and an example. Front-loaded with the most important information. No extraneous text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of verifiable randomness and the presence of an output schema, the description is complete. It covers purpose, usage, return format, proof structure, verification method, and cost. All relevant aspects for an agent to use the tool correctly are addressed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, baseline 3. The description adds semantic value: 'Use 32 for a 256-bit seed/word' and explains client_seed's role in domain separation ('two callers asking at the same tick get distinct, attributable randomness'). This goes beyond schema details, justifying a 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it draws unbiased, Ed25519-signed verifiable randomness. The verb 'draw' and resource 'unbiasable...randomness (Platon)' are specific. It distinguishes itself from siblings by emphasizing verifiability and unbiasability, though not explicitly naming alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly lists when to use: 'for fair selection, sampling, raffles, commit-reveal, anti-MEV ordering'. Provides context for verifiable randomness. Lacks explicit when-not-to-use or direct comparisons to siblings, but the use cases are clear.

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

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