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notarize_inference

Cryptographically notarize an AI inference and receive a signed Ed25519 attestation, sha256 content hash, and Merkle chain-anchor status. Costs $0.001 USDC.

Instructions

Get a cryptographic receipt for one AI inference. Returns a signed Ed25519 attestation, sha256 content hash, and Merkle chain-anchor status for {prompt, response, model_id}. The notary does NOT store your prompt or response — only the hash is retained. Costs $0.001 USDC via x402 (requires WALLET_PRIVATE_KEY for Base or SOLANA_PRIVATE_KEY for Solana).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesThe exact prompt/input that was sent to the model
model_idYesModel identifier, e.g. 'openai/gpt-5' or 'claude-fable-5'
responseYesThe exact model output you want a receipt for
client_timestampNoOptional ISO-8601 time the inference ran. Included in the content hash if provided.
Behavior4/5

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

Discloses that only hash is retained, not prompt/response, and mentions cost and required keys. Missing details on idempotency or error scenarios, but overall transparent.

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?

Three sentences, no fluff. First sentence clearly states purpose, second details output, third provides key behavioral info. Front-loaded efficiently.

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?

For a tool with no output schema, description adequately explains return value and notable behaviors. Could mention potential errors or atomicity, but still quite complete.

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 all parameters with 100% description. Description adds context about receipt contents (sha256, Ed25519) but does not significantly enhance parameter semantics beyond 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 clearly states 'Get a cryptographic receipt for one AI inference' with specific verb and resource. Differentiates from siblings like 'notarize_batch' by focusing on single inference.

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

Usage Guidelines3/5

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

Cost and required wallet keys are mentioned, but no explicit when-to-use vs alternatives or exclusions. Sibling tools provide context, but description itself lacks direct 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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