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Baseline Safety Check

dcl_evaluate_safety

Assess an AI response for safety by checking the required 'AI' disclosure and absence of two forbidden disclaimer phrases; returns NO_COMMIT if confidence falls below 0.75.

Instructions

PRE-ACTION Baseline Safety Check ($0.01). Runs the "safety" policy: flags 2 forbidden disclaimers ("I cannot be held responsible", "no guarantees") and additionally REQUIRES the substring "AI" to appear somewhere in the response — missing it costs 0.2 confidence even with no forbidden phrase present. Minimum confidence is 0.75. Returns NO_COMMIT if confidence drops below 0.75, with reason naming the forbidden phrase found or the missing required pattern. Use this when you specifically need to confirm an AI-disclosure marker is present and the two disclaimer phrases are absent — not as a general-purpose safety net; for broader coverage use dcl_evaluate_fast or dcl_evaluate_strict instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesIdentifier of the agent that produced the response.
responseYesThe agent or LLM response text to check for safety violations.
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.
Behavior5/5

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

The description discloses many behavioral details beyond the annotations, including the cost ($0.01), the exact policy thresholds, the 0.2 confidence penalty for missing 'AI', the minimum confidence of 0.75, and the NO_COMMIT return behavior with a reason. This far exceeds the bare annotation hints.

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?

The description is dense but every sentence adds value. It front-loads the essential purpose and cost, then details the policy, thresholds, return behavior, and usage guidance without any fluff. Despite being long, it is efficiently structured and easy to parse.

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

Completeness5/5

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

Given the tool's complexity, the description fully covers the evaluation criteria, confidence calculation, return values, and usage boundaries. The output schema exists and would explain return format, but the description still clarifies the NO_COMMIT condition and reason, making it complete for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% description coverage for all parameters, so the baseline is 3. The description adds meaningful context by explaining how the 'response' text is evaluated against the safety policy and how confidence is calculated, enriching the parameter semantics beyond simple field descriptions.

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 tool's function: runs the 'safety' policy, flags two forbidden disclaimers, requires the substring 'AI', and returns NO_COMMIT when confidence drops below 0.75. It also distinguishes from siblings by explicitly noting it is not a general-purpose safety net and pointing to dcl_evaluate_fast or dcl_evaluate_strict for broader coverage.

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

Usage Guidelines5/5

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

The description gives explicit when-to-use guidance: 'Use this when you specifically need to confirm an AI-disclosure marker is present and the two disclaimer phrases are absent.' It also names alternatives for broader coverage, providing clear context and exclusions.

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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