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compute_vdf

Evaluate a Verifiable Delay Function to produce provable proof of elapsed sequential work, ensuring fair ordering, timed reveals, and unpredictable randomness.

Instructions

Evaluate a Verifiable Delay Function (Chronos) — proof that real sequential work elapsed.

Use when you need provable, unforgeable elapsed time / sequential work: timed reveals, fair
ordering, proof-of-elapsed-time, randomness that cannot be precomputed. Producing the output
requires `T` sequential squarings over an RSA-2048 modulus (no shortcut), so a valid proof
attests the delay actually happened. Verify cheaply with `verify_vdf`.

Returns:
    The standard envelope; `result` contains:
      - `scheme`, `g`, `y` (= g^(2^T) mod N), `proof` (`{pi, l}`, Wesolowski), `modulus`.
    `verifiable.has_proof` will be true. Cost ~$0.01 USDC.

Example:
    compute_vdf(seed="0x1234abcd", difficulty=100000)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedYesSeed (hex) the VDF is evaluated over; it binds the generator g, so the output is tied to this input.
difficultyNoT — the number of sequential squarings to perform (1..1_000_000). Higher T = more wall-clock work that cannot be parallelized/GPU-accelerated, i.e. a longer provable delay. ~1000 is a fast demo; tune T to the delay you need.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, so description carries full burden. Describes that output requires T sequential squarings (no shortcut), attests delay, returns specific fields in envelope, and mentions cost ~$0.01 USDC. Comprehensive behavioral disclosure beyond schema.

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?

No fluff. Clear sections: purpose, usage, returns, example. Every sentence adds value. Well-structured for quick parsing.

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?

With 2 params, 100% schema coverage, and output schema present, description fully explains all aspects: what it does, when to use, parameter semantics, return values, and cost. No gaps for an agent to select and invoke correctly.

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 good descriptions. Description adds extra context: seed binds generator g, difficulty is T with range and wall-clock implication, and notes it cannot be parallelized/GPU-accelerated. This enhances understanding beyond schema alone.

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?

Clearly states 'Evaluate a Verifiable Delay Function (Chronos)' and distinguishes from sibling `verify_vdf`. Provides specific verb ('compute' implied by name) and resource (VDF). Context signals and sibling list confirm differentiation.

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

Usage Guidelines5/5

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

Explicitly says 'Use when you need provable, unforgeable elapsed time/sequential work' with examples like timed reveals, fair ordering. Mentions verifying cheaply with `verify_vdf`, giving clear when-to-use and when-not-to guidance.

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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