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Radiant-Core

Radiant MCP Server

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by Radiant-Core

radiant_create_inference_proof

Generate a Blake3 hash commitment binding model, input, and output to record AI inference results on-chain via the InferenceProof contract.

Instructions

Create a blake3 inference proof commitment: hash(modelHash || inputHash || output). Used to record AI inference results on-chain via the InferenceProof contract.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
input_hashYesBlake3 hash of the input data (64 hex chars)
model_hashYesBlake3 hash of the model weights (64 hex chars)
output_hexYesInference output as hex string

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.6.0

TDQS

A3.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does mention an on-chain contract effect and the hash composition, but it omits important behavior such as whether this broadcasts a transaction, requires wallet/key setup, incurs fees, or is irreversible. For a state-changing tool, this is a notable gap.

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 a single tight sentence, starts with the core action, and includes the precise formula without filler. Every clause contributes useful information about what the tool does and why.

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

Completeness3/5

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

For a relatively simple three-parameter tool with full schema coverage, the core call is documented. However, there is no output schema and no mention of return values, prerequisites, or on-chain side effects, so the description is adequate but not complete for an agent invoking a mutation-style tool.

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

Parameters3/5

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

Input schema coverage is 100%, so the schema already describes all three parameters in detail. The description adds the hash ordering (modelHash || inputHash || output), which is useful, but it does not meaningfully expand on parameter formats or constraints beyond the schema.

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?

Description states a specific verb and resource: 'Create a blake3 inference proof commitment' with an explicit formula 'hash(modelHash || inputHash || output)'. It also names the contract purpose, distinguishing it from the sibling verify_inference_proof tool by focusing on recording/creating on-chain commitments.

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 phrase 'Used to record AI inference results on-chain via the InferenceProof contract' gives clear contextual guidance for when this tool is appropriate. It does not explicitly state when not to use it or name the verify sibling as the alternative, so it falls just short of a 5.

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