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PII Detection Scan

dcl_evaluate_pii

Scan agent responses for personal data across 8 categories, from emails to crypto addresses. Any finding triggers a NO_COMMIT verdict, halting risky commits.

Instructions

POST-ACTION PII Detection Scan ($0.02). Regex-based scan across 8 personal-data categories, with a Luhn checksum on card numbers to reduce false positives. Any finding results in NO_COMMIT.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesIdentifier of the agent that produced the response.
responseYesThe text to scan for personal data: emails, phone numbers, national IDs, bank cards, IBANs, crypto addresses, IP addresses, passport numbers.
payment_idNoOptional payment identifier returned by a previous call when payment is required

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
tx_hashYesHash of this record in the tamper-evident audit chain.
verdictYesCOMMIT if nothing was found, otherwise NO_COMMIT.
findingsYesAll matches found. Empty list if verdict is COMMIT.
seal_textYesHuman-readable Leibniz Layer verification seal.
timestampYesUnix timestamp when this record was sealed.
input_hashYesHash of the scanned text (raw content is never stored).
risk_scoreYes0.0-1.0 risk score based on number and severity of findings.
verify_urlYesPublic URL to independently verify this seal.
chain_indexYesSequential index of this record in the audit chain.
detection_countYesNumber of findings.
categories_clearYesCategories with no findings.
categories_checkedYesAll checklist categories that were scanned.
Behavior5/5

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

Annotations only state readOnlyHint=false, destructiveHint=false, providing no safety profile. The description compensates fully by disclosing the cost ($0.02), method (regex-based, Luhn checksum), and critical behavioral consequence (any finding results in NO_COMMIT). This adds substantial context beyond annotations and no contradiction exists.

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, each earning its place: states the purpose/cost, explains the scan mechanics and false-positive reduction, and reveals the key outcome (NO_COMMIT). Front-loaded with the tool's function, no wasted language.

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?

Given the presence of a true output schema and moderate complexity, the description is largely complete: it covers purpose, cost, mechanism, and outcome. It could be more explicit about the expected workflow (e.g., when exactly to call it relative to other actions), but the 'POST-ACTION' and NO_COMMIT signals convey the essential context.

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 description coverage is 100%, with all three parameters (agent_id, response, payment_id) already described clearly. The description adds no additional parameter-specific meaning beyond what the schema provides, so the baseline of 3 applies.

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 states a clear purpose with a specific verb ('Detection Scan') and resource ('PII'), and explicitly names the scope: scanning across 8 personal-data categories. It distinguishes itself from siblings by focusing on PII and mentions a unique method (Luhn checksum) and outcome (NO_COMMIT).

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?

The 'POST-ACTION' prefix implies when to use, and the NO_COMMIT outcome suggests it blocks commits, but there is no explicit statement of when to use versus alternatives or exclusions. No mention of other sibling tools or criteria for choosing this over dcl_evaluate_secrets or dcl_evaluate_safety.

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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