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judge_receipt

Create a pending judgment receipt for evaluating AI output quality against a rubric. Supports weighted criteria, partial verdicts, and confidence scores for nuanced assessment.

Instructions

Start an AI judgment evaluation for a receipt by creating a pending judgment receipt and returning a structured evaluation prompt. The host model (you) evaluates the receipt's output against the provided rubric criteria and then calls complete_judgment with the results. Use to assess output quality beyond simple pass/fail constraints — supports weighted criteria, partial verdicts, and confidence scores. Judgment receipts are themselves Ed25519-signed for auditability.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
receipt_idYesThe receipt ID to evaluate — the original action receipt
rubricYesEvaluation rubric with criteria array. Each criterion needs: name (string), description (string), weight (0.0-1.0), and optional passing_threshold (0.0-1.0, default 0.7). Also set: passing_threshold (overall, default 0.7) and require_all (boolean, default false)
output_summary_for_reviewNoThe actual output content to evaluate — provide if output_summary on the receipt is insufficient for evaluation
Behavior4/5

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

With no annotations, the description carries full burden. It discloses the workflow: creating a pending judgment, returning a prompt, requiring a subsequent complete_judgment call, and that receipts are Ed25519-signed. This provides good transparency.

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, front-loaded with purpose and structured details. No redundant information. Every sentence adds value.

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?

No output schema exists. The description mentions returning a structured evaluation prompt but does not detail its format. However, given the complexity (nested rubric), it provides sufficient high-level completeness.

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 baseline is 3. The description adds some context (e.g., host model evaluates, output_summary_for_review when insufficient), but the schema already adequately describes the parameters.

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 verb 'Start an AI judgment evaluation' and the resource 'a receipt'. It distinguishes itself by mentioning the creation of a pending judgment receipt and the need to later call complete_judgment, differentiating it from siblings like verify_receipt.

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 advises using this tool for assessing output quality beyond simple pass/fail, implying when it is appropriate. It does not explicitly list alternatives, but the contrast with 'simple pass/fail' provides usage context.

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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