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

Market Signal Fabrication Screen

dcl_evaluate_signal

POST-ACTION Market Signal Fabrication Screen ($0.03). Pattern-based heuristic on the output text alone (no source price feed) — flags guaranteed-price-prediction language ("will definitely hit $X"), absolute-certainty claims ("100% certain", "cannot go down"), a fabricated-price flag when a specific dollar figure co-occurs with a guaranteed-outcome claim, and an invented-token flag when a "$TICKER" cashtag doesn't match a small set of well-known symbols (false positives are possible for legitimate lesser-known tickers — this is a heuristic pre-check, not ground truth). For a full claim-by-claim check against an actual price-feed snapshot, use the local grounding workflow instead of this live tool. Verdict/confidence collapsing follows the same rule as dcl_evaluate_mev: any critical finding or 2+ major findings is a hard NO_COMMIT; exactly one major finding is a softer NO_COMMIT at ~0.55 confidence.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`agent_id`	Yes	Identifier of the agent that produced the response.
`response`	Yes	The market signal, analysis, or price-prediction text to screen.

Output Schema

TableJSON Schema

Name	Required	Description
`reason`	Yes	Human-readable explanation of the verdict.
`tx_hash`	Yes	Hash of this record in the tamper-evident audit chain.
`verdict`	Yes	COMMIT if no fabrication/overconfidence pattern matched, otherwise NO_COMMIT.
`findings`	Yes	All matched patterns. Empty list if verdict is COMMIT.
`timestamp`	Yes	Unix timestamp when this record was sealed.
`confidence`	Yes	Confidence score of the verdict, from 0.0 to 1.0.
`input_hash`	Yes	Hash of the screened text (raw content is never stored).
`chain_index`	Yes	Sequential index of this record in the audit chain.

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

A4.4/5.0

Behavior4/5

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

With no useful annotation hints (readOnly/destructive/idempotent all false), the description carries the burden and discloses key traits: it runs 'on the output text alone (no source price feed)', is not ground truth, and has false positives for lesser-known tickers. It also explains the verdict/confidence collapsing rule. It doesn't mention side effects, but this is a screening tool and no modification is implied.

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

Conciseness4/5

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

Dense single paragraph is front-loaded with title and cost, and each clause adds information about inputs, limitations, alternatives, or decision logic. It is longer than strictly necessary and could be broken into bullets, but there is no filler.

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?

For a 2-parameter tool with an output schema, the description is operationally sufficient: it defines inputs, behavior, limitations, cost, alternative workflow, and output decision rules. Nothing required to select and call the tool 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 covers 100% of parameters, so baseline is 3. The description does not add parameter-level semantics beyond what the schema already states, though it reinforces that 'response' is the text being screened.

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 names a specific verb+resource: it screens market signal text for fabrication. It details four concrete flags and explicitly contrasts itself with 'the local grounding workflow' and references 'dcl_evaluate_mev', so an agent can distinguish it from siblings.

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?

It explicitly states when to prefer the alternative: 'For a full claim-by-claim check against an actual price-feed snapshot, use the local grounding workflow instead of this live tool.' It also characterizes itself as a 'heuristic pre-check', implying the quick-screening use case.

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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