DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)

PII Detection Scan

dcl_evaluate_pii

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

Name	Required	Description	Default
`agent_id`	Yes	Identifier of the agent that produced the response.
`response`	Yes	The text to scan for personal data: emails, phone numbers, national IDs, bank cards, IBANs, crypto addresses, IP addresses, passport numbers.

Output Schema

TableJSON Schema

Name	Required	Description
`tx_hash`	Yes	Hash of this record in the tamper-evident audit chain.
`verdict`	Yes	COMMIT if nothing was found, otherwise NO_COMMIT.
`findings`	Yes	All matches found. Empty list if verdict is COMMIT.
`seal_text`	Yes	Human-readable Leibniz Layer verification seal.
`timestamp`	Yes	Unix timestamp when this record was sealed.
`input_hash`	Yes	Hash of the scanned text (raw content is never stored).
`risk_score`	Yes	0.0-1.0 risk score based on number and severity of findings.
`verify_url`	Yes	Public URL to independently verify this seal.
`chain_index`	Yes	Sequential index of this record in the audit chain.
`detection_count`	Yes	Number of findings.
`categories_clear`	Yes	Categories with no findings.
`categories_checked`	Yes	All checklist categories that were scanned.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedAug 6, 2026

removedInput schema / properties / payment_id

Removed value: -{
-  "default": "",
-  "description": "Optional payment identifier returned by a previous call when payment is required",
-  "title": "Payment Id",
-  "type": "string"
-}

First observedAug 5, 2026

TDQS

A3.8/5.0

Behavior4/5

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

With all annotations false, the description carries the transparency burden and does meaningful work: it discloses that the scan is regex-based, covers 8 categories, applies a Luhn checksum for card numbers, and reports NO_COMMIT on any finding. It does not describe the output shape, but an output schema exists and there is no annotation contradiction.

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 short sentences front-load the action, cost, mechanism, and the critical NO_COMMIT consequence. There is no filler or repetition of schema content.

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 simple two-parameter scan with a complete input schema and an output schema, the description provides cost, method, category scope, and the effect of findings. It is slightly incomplete in not routing the agent away from adjacent evaluate tools, but no fundamental calling detail is missing.

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 baseline is 3 because both parameters are already fully documented. The description adds processing context such as regex categories and Luhn validation, but it does not add new meaning about agent_id or the input format beyond what the schema provides.

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

Purpose4/5

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

The description clearly identifies an 8-category PII scan with regex methodology and a NO_COMMIT consequence, so the core purpose is obvious. It does not explicitly differentiate from closely related siblings like dcl_evaluate_secrets, but the PII focus and method make the tool's role specific.

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' label and NO_COMMIT outcome imply it belongs after an action and before committing, which is useful contextual guidance. However, the description never states when to prefer this over alternatives or which sibling tools cover adjacent concerns such as secrets and 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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