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cmdenney

@logicnodez/mcp-bridge

by cmdenney

logicnodes_inference_attest

Attest AI inference results on-chain by submitting model ID and input/output hashes. Produces a verifiable POL receipt for $0.001 USDC.

Instructions

Attest an AI inference result on-chain via InferenceAttestationNetwork. Produces a signed POL receipt verifiable by any chain participant. Cost: $0.001 USDC per attestation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYes
confidenceNoModel confidence 0-1
input_hashYeskeccak256 of inference input
output_hashYeskeccak256 of inference output
Behavior2/5

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

Discloses cost ($0.001 USDC) and mentions the receipt, but lacks details on side effects, permissions, or error scenarios. No annotations provided, so description carries full burden but is insufficient.

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?

Two concise sentences, front-loaded with verb, no redundant information. Every word adds value.

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

Completeness2/5

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

Without output schema or annotations, the description should provide more context on usage, return values, and error handling. It is incomplete for a tool with 4 parameters.

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?

Schema covers 75% of parameters with descriptions. Description adds no additional parameter information beyond what is in the input schema, so baseline 3 applies.

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

Purpose4/5

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

Description clearly states the tool attests an AI inference result on-chain and produces a signed POL receipt. It specifies the platform and output, but does not explicitly distinguish from sibling tools like zk_compute_attest.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. The description does not indicate prerequisites, exclusions, or context for usage.

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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