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

Content Quality & Drift Check

dcl_evaluate_quality

PRE-ACTION Content Quality & Drift Check ($0.03). Runs the "content_quality" policy: flags 12 absolutist or unverifiable-claim phrases (e.g. "guaranteed returns", "100% accurate", "studies show", "without a doubt") with a 0.85 minimum-confidence threshold — the highest bar of any single-policy tool. Returns NO_COMMIT if any phrase matches or confidence falls below 0.85, with reason listing the matched phrase(s). Use this to catch overconfident or unsubstantiated claims in generated content — a different concern from jailbreak or safety phrasing — e.g. before publishing agent-written copy or reports.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`agent_id`	Yes	Identifier of the agent that produced the response.
`response`	Yes	The agent or LLM response text to check for quality and drift.

Output Schema

TableJSON Schema

Name	Required	Description
`reason`	Yes	Human-readable explanation of why the verdict was reached.
`tx_hash`	Yes	Hash of this record in the tamper-evident audit chain.
`verdict`	Yes	COMMIT if the response passed policy checks, otherwise NO_COMMIT.
`seal_text`	Yes	Human-readable Leibniz Layer verification seal.
`timestamp`	Yes	Unix timestamp when this record was sealed.
`confidence`	Yes	Confidence score of the verdict, from 0.0 to 1.0.
`drift_mode`	Yes	Current drift status: NORMAL, WARNING, ESCALATION, or BLOCK.
`input_hash`	Yes	Hash of the evaluated response (raw content is never stored).
`verify_url`	Yes	Public URL to independently verify this seal.
`chain_index`	Yes	Sequential index of this record in the audit chain.
`drift_score`	Yes	Z-score measuring deviation of the recent commit rate from baseline.
`policy_version`	Yes	Version of the policy that was applied.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

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

A4.7/5.0

Behavior5/5

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

Annotations carry little behavioral weight here (all hints false), so the description carries the burden and does so thoroughly: it discloses the $0.03 cost, the 0.85 minimum-confidence threshold, the NO_COMMIT return condition, and the `reason` output field. This gives the agent concrete expectations beyond what the schema provides.

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 efficient, packing the threshold, phrase set, examples, return behavior, cost, and intended use into a short paragraph. Every sentence contributes purposeful guidance with 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?

The tool is fully described in context: what it checks, when to use it, how it differs from siblings, what triggers NO_COMMIT, and what the reason field contains. The output schema exists and the description still sketches the key return behavior, so an agent can select and invoke it confidently.

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%, so the baseline is 3. The description does not add significant parameter-specific meaning beyond framing the response as generated content, but the schema already documents both parameters clearly.

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 names the specific resource ('content_quality' policy) and the exact action: flagging 12 absolutist or unverifiable-claim phrases. It also differentiates this from jailbreak or safety phrasing, making it easy to distinguish from sibling evaluate tools.

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 says 'Use this to catch overconfident or unsubstantiated claims in generated content' and gives a concrete use case: before publishing agent-written copy or reports. It also draws a boundary by noting this is 'a different concern from jailbreak or safety phrasing.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0

Disambiguation3/5

There is meaningful overlap among the many dcl_evaluate_* variants (e.g., fast/strict/jailbreak/safety, secrets/wallet/pii, mev/signal/trade), so an agent could easily pick the wrong one. The descriptions are detailed and do help, but the boundaries between policies are subtle enough that the set is not clearly disambiguated.

Naming Consistency4/5

The dcl_evaluate_* family is highly consistent, and the remaining tools (dcl_commit, dcl_audit_decode, dcl_audit_decode_deep, dcl_pipeline_start) still follow the same snake_case dcl_ prefix convention. Minor deviations like using adjectives/adverbs in evaluate_fast/strict and noun-verb ordering in dcl_pipeline_start keep it from a perfect 5.

Tool Count3/5

18 tools is on the heavy side, especially since many are single-policy evaluators that could arguably be consolidated behind a policy parameter. That said, the tool count is justified by distinct pricing tiers and the breadth of audit categories, so it is borderline rather than excessive.

Completeness4/5

The audit lifecycle is well covered: pre-action evaluation, post-action scans, sanitization, final commit, and on-chain retrieval/verification. Minor gaps exist—no list/query tool for the append-only chain and no way to define custom policies—but the core workflows have no dead ends.

Content Quality & Drift Check

Input Schema

Output Schema

Schema Changelog

TDQS

Glama MCP Gateway

TDQS

Resources