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ask_oracle

Get unbiased, entropy-driven answers for random picks or simple decisions using a verifiable oracle. Read-only, no side effects.

Instructions

Ask the Platon oracle for a grounded, entropy-derived read-only answer.

Use for lightweight oracle-mediated decisions (e.g. an unbiased pick among options). Read-only —
no side effects, no state change.

Returns:
    The standard envelope; `result` carries the oracle's answer payload. Cost ~$0.003 USDC.

Example:
    ask_oracle(question="Pick one at random: red, green, or blue?")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYesA question to answer from the oracle's entropy/state. Read-only; no side effects.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description transparently states read-only behavior, no side effects, and even discloses cost (~$0.003 USDC). It lacks details on rate limits or error handling, but for a simple read operation, this is sufficient.

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?

The description is highly concise: two short paragraphs plus a code example. Every sentence adds value—purpose, usage, return details, cost, and example—with clear structure and no redundancy.

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 the tool's simplicity (single parameter, output schema exists), the description covers purpose, usage, behavior, return format, and cost. Minor missing details like synchronous behavior or timeout, but overall complete for effective agent invocation.

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%, so baseline is 3. The description adds value by providing an example of a valid question and explaining the type of answer expected (entropy-derived, unbiased pick), which goes beyond the schema's documentation.

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 the tool's purpose: 'Ask the Platon oracle for a grounded, entropy-derived read-only answer.' It specifies a specific verb-resource pair and differentiates from siblings like get_random and verify_random by focusing on oracle-mediated decisions.

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?

The description provides context on when to use: 'lightweight oracle-mediated decisions (e.g. an unbiased pick among options)' and emphasizes it is read-only. While it doesn't explicitly exclude scenarios, the sibling tools (compute_vdf, get_randomness_beacon, etc.) suggest alternative use cases.

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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