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notarize_batch

Notarize up to 20 AI outputs in one call, producing signed attestations per record for audit trails and agent pipelines. Costs $0.005 USDC via x402.

Instructions

Notarize up to 20 AI inferences in one call — one signed attestation per record. Ideal for audit trails and agent pipelines. Costs $0.005 USDC via x402 (requires WALLET_PRIVATE_KEY for Base or SOLANA_PRIVATE_KEY for Solana).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
recordsYes1-20 records of {prompt, response, model_id, client_timestamp?}
Install Server

TDQS

A4.2/5.0
Behavior4/5

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

With annotations declaring readOnlyHint=false (mutation), the description adds valuable disclosure beyond what annotations provide: the operation costs $0.005 USDC, requires WALLET_PRIVATE_KEY or SOLANA_PRIVATE_KEY, and produces persistent signed attestations. This surfaces the cost and auth prerequisites an agent needs before invoking, which is genuinely helpful and consistent with the annotations.

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 sentences with zero waste: the core capacity and benefit are front-loaded, and the cost/auth detail is appended. Every clause earns its place and no structured data is repeated.

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 single-parameter tool with nested objects fully documented in the schema, the description covers purpose, capacity, use case, cost, and auth. The only gap is the response/return format (no output schema exists), which is a minor omission given the tool's simplicity and the richness of the annotations.

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 coverage is 100%, so the schema fully documents prompt, model_id, response, and client_timestamp including the content-hash inclusion caveat. The description does not add meaning beyond the schema, so the baseline of 3 applies—the schema carries the burden and does so adequately.

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?

States a specific verb (notarize), a specific resource (AI inferences), and a batching capacity (up to 20, one signed attestation per record). The phrase 'Ideal for audit trails and agent pipelines' conveys the intended use case. It clearly differentiates from notarize_inference via the batch capacity, so an agent can tell them apart.

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?

Provides clear context ('Ideal for audit trails and agent pipelines') that an agent can use to decide when batching is appropriate. However, it does not explicitly name the sibling notarize_inference or state the trade-off (single vs. batch), so the exclusion is implied rather than explicit.

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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