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verify_random

Verify a Platon randomness draw by confirming the Ed25519 signature over the random hex and proof against the signer's public key, ensuring trust without re-execution.

Instructions

Verify a Platon randomness draw without re-running it (Platon verify).

Pass `random_hex`, `proof`, and `signature` exactly as returned by `get_random` /
`get_randomness_beacon`. Confirms the Ed25519 signature over the canonical (random_hex, proof)
against the signer's published public key — so you trust the math, not the service.

Returns:
    The standard envelope; `result` is `{valid: <bool>}` (plus `error` if the input was
    malformed). Cost ~$0.001 USDC.

Example:
    verify_random(random_hex="0x9f2c…", proof={"scheme": "platon-chaos-vrf/v1", …},
                  signature={"algorithm": "ed25519", "public_key": "<b64>", "value": "<b64>"})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
random_hexYesThe `random_hex` value returned by get_random / get_randomness_beacon.
proofYesThe `proof` object from the draw: {scheme, state_hash, client_seed, tick, timestamp, entropy_commitment}.
signatureYesThe `signature` object from the draw: {algorithm:'ed25519', public_key, value}.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations, so description fully bears burden. Discloses Ed25519 signature verification, cost (~$0.001 USDC), and return format (valid bool). Does not mention authentication or rate limits, but adequate.

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

Conciseness4/5

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

Front-loaded with purpose, followed by usage details, return info, and example. Every sentence adds value; example is helpful but slightly lengthy. Overall efficient.

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

Completeness4/5

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

Given output schema exists (so return values defined) and parameters well-described, the description provides sufficient additional context: verification method, cost, and requirement for exact parameter copying. Complete for the tool's complexity.

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% with descriptions and examples; description adds context that parameters must be exact copies from get_random/get_randomness_beacon and provides a concrete example, enhancing clarity.

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 verifies a Platon randomness draw without re-running, using specific verb and resource. It distinguishes from siblings like get_random (which produces data) and verify_vdf.

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 instructs to pass parameters exactly as returned by get_random/get_randomness_beacon, providing clear usage context. Does not explicitly mention when not to use, but contrast with re-running implies alternative.

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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