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Strict Pre-Action Audit

dcl_evaluate_strict

Audit an agent's response pre-action to verify policy compliance with a high-confidence threshold, preventing unsafe or unauthorized AI outputs.

Instructions

STRICT Pre-Action Audit ($0.05). Rigorous check with a higher confidence threshold.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesIdentifier of the agent that produced the response.
responseYesThe agent or LLM response text to audit.
payment_idNoOptional payment identifier returned by a previous call when payment is required

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonYesHuman-readable explanation of why the verdict was reached.
tx_hashYesHash of this record in the tamper-evident audit chain.
verdictYesCOMMIT if the response passed policy checks, otherwise NO_COMMIT.
seal_textYesHuman-readable Leibniz Layer verification seal.
timestampYesUnix timestamp when this record was sealed.
confidenceYesConfidence score of the verdict, from 0.0 to 1.0.
drift_modeYesCurrent drift status: NORMAL, WARNING, ESCALATION, or BLOCK.
input_hashYesHash of the evaluated response (raw content is never stored).
verify_urlYesPublic URL to independently verify this seal.
chain_indexYesSequential index of this record in the audit chain.
drift_scoreYesZ-score measuring deviation of the recent commit rate from baseline.
policy_versionYesVersion of the policy that was applied.
Behavior2/5

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

Annotations are all false and offer no safety profile. The description adds only the cost ($0.05) and that it's a 'rigorous check', but does not mention return behavior, side effects, or whether payment_id is required. This is insufficient to understand the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise at two sentences, but it is under-specified. 'STRICT Pre-Action Audit' and 'Rigorous check' are somewhat redundant, and the sentence adds only cost and confidence threshold without elaborating.

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?

With an output schema present, return values are defined elsewhere, but the description still lacks context about the audit scope, how it differs from other dcl_evaluate_* tools, and when to use it. It feels incomplete for a tool with multiple siblings.

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?

The input schema covers all parameters with descriptions (100% coverage), so the baseline is 3. The description itself does not add parameter details, but the schema sufficiently explains 'response', 'agent_id', and 'payment_id'.

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

Purpose3/5

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

The description identifies this as a 'Pre-Action Audit' and a 'Rigorous check with a higher confidence threshold', but does not specify what exactly is being audited or what criteria are used. It distinguishes from siblings only by 'strict' and 'higher confidence threshold', but lacks a clear verb+resource like 'evaluates the response for violations'.

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 is provided on when to use this tool over alternatives like dcl_evaluate_fast or dcl_evaluate_safety. The phrase 'higher confidence threshold' implicitly suggests a stricter scenario, but no explicit usage context or exclusion criteria is given.

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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