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

Name: DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)
Author: Fronesis-Labs

by Fronesis-Labsversion 2.3.1i★ –iRuns in the cloud

Registry namecom.fronesislabs/dcl-trust-oracle

Install

Overview

Description from the maker (original English)

AI/LLM agent output audit MCP: policy eval, tamper-evident chain, AI safety, x402 USDC on Base.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs in the cloud
Transport: Streamable HTTP
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on the maker’s server: what you ask it goes through their server.
The registry lists no key to fill in.

You need

Runs in the cloudNothing to install; it connects over the internet

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Works in Claude on the web, desktop and mobile:

Open Settings → Connectors
Press Add custom connector
Paste the address below and press Add
If it asks you to sign in, sign in with the maker

https://mcp.fronesislabs.com/mcp

Run this in your terminal

claude mcp add --transport http dcl-trust-oracle https://mcp.fronesislabs.com/mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "dcl-trust-oracle": {
      "url": "https://mcp.fronesislabs.com/mcp"
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "dcl-trust-oracle": {
      "type": "http",
      "url": "https://mcp.fronesislabs.com/mcp"
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

ChatGPT connects only to servers on the internet:

Open ChatGPT on the web → Settings → Connectors (Apps)
Press Create or Add (some plans need Developer mode under Advanced first)
Paste the address below, give it a name and save
If it asks you to sign in, sign in with the maker

https://mcp.fronesislabs.com/mcp

Menu names can differ slightly by plan and over time.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

We have not read this server’s tool list yet; we check endpoints in rotation.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified against the domain fronesislabs.com (the registry checks domain ownership)
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
… Not checked yet
Endpoint responds
Not checked yet; we check endpoints in rotation
… Not checked yet
Sign-in with OAuth
Not checked yet; we check endpoints in rotation
… Not checked yet
No hidden instructions in tool descriptions
Not checked yet; we read the tool list when we check the endpoint

Compared with others in Search & AI memoryi

More stars than 0% of the other 7259 servers in this category
Published to the registry more recently than 30% of the other 7259 servers in this category
Basic checks equal to or better than 100% of the other 7259 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

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

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs in the maker’s cloud. Connect over the internet with nothing to install, but what you send passes through the maker’s servers.

Is it still maintained?

The GitHub repo last changed –, and the latest version (2.3.1) reached the registry 3 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

DCL Trust Oracle

Don't trust the agent. Trust the proof.

Autonomous AI agents now take actions with real consequences — financial, legal, reputational. Most of them are black boxes: no record of what was decided, why, or whether that decision was tampered with afterward.

DCL Trust Oracle closes that gap. Every agent output is evaluated against policy in real time and sealed into a tamper-evident hash chain — a deterministic, cryptographically verifiable record of what happened and when. Edit any past entry and the entire chain invalidates. No one — not even Fronesis Labs — has to be trusted for the record to hold up.

What It Does

DCL Trust Oracle provides deterministic policy evaluation for LLM outputs with a tamper-evident audit chain. The system stores only cryptographic hashes and decision metadata — never raw content — enabling verifiable, post-action forensic analysis across distributed AI agents.

Available two ways:

REST API (webhook_server.py) — direct HTTP integration.
MCP Server (mcp_server.py) — native Model Context Protocol integration for AI agents. Live at https://mcp.fronesislabs.com/mcp (streamable-http). Also listed on Smithery (remote URL → same production endpoint; see smithery.yaml) and the official MCP Registry.

Both servers share the same evaluation logic and tamper-evident chain (dcl_core.py), and are priced identically.

Quick Start

REST API

pip install -r requirements.txt

python webhook_server.py

Server runs on `http://localhost:8080`

MCP Server

Production (hosted):

https://mcp.fronesislabs.com/mcp

Streamable HTTP transport — point any MCP client here directly, no setup required.

Local development:

pip install -r requirements.txt

python mcp_server.py

Server runs on `http://localhost:8081` (streamable-http transport)

Tools & Endpoints

Pre-Action Evaluation

Catch a bad output *before* it reaches a user, a wallet, or downstream system.

REST Endpoint	MCP Tool	Price	Description
`POST /evaluate/fast`	`dcl_evaluate_fast`	$0.01	Fast policy check for low-risk outputs. Returns tamper-evident `tx_hash`.
`POST /evaluate/strict`	`dcl_evaluate_strict`	$0.05	Deep analysis for high-stakes outputs with higher confidence thresholds.
`POST /evaluate/jailbreak`	`dcl_evaluate_jailbreak`	$0.02	Instruction adherence check — detects prompt injection patterns and role-hijacking attempts.
`POST /evaluate/safety`	`dcl_evaluate_safety`	$0.01	Baseline screening for known harmful text patterns. Optimized for high throughput.
`POST /evaluate/quality`	`dcl_evaluate_quality`	$0.03	Content quality & drift check — evaluates format adherence and contextual drift.
`POST /evaluate/batch`	`dcl_evaluate_batch`	$0.10	Bulk processing — up to 200 items per MCP call (REST default `max_items`: 20). Cost-effective for multi-turn history.

Session Management

REST Endpoint	MCP Tool	Price	Description
`POST /pipeline/start`	`dcl_pipeline_start`	$0.05	Initializes a long-running audit session for continuous drift tracking. Returns `pipeline_id`.

Post-Action Forensics

When something *did* go wrong, reconstruct exactly what happened.

REST Endpoint	MCP Tool	Price	Description
`GET /audit/{tx_hash}`	`dcl_audit_decode`	$0.10	Basic post-action audit — returns verdict, confidence, agent_id, reason by `tx_hash`.
`GET /audit/{tx_hash}/deep`	`dcl_audit_decode_deep`	$0.50	Deep forensic audit — includes drift context, tamper-evidence indices, environmental metadata.

Post-Action Scans

REST Endpoint	MCP Tool	Price	Description
`POST /evaluate/secrets`	`dcl_evaluate_secrets`	$0.02	Secret & credential leak scan.
`POST /evaluate/pii`	`dcl_evaluate_pii`	$0.02	PII detection scan.

DCL Update Sentinel (Continuous Skill Monitoring)

Continuous security auditing and release verification for AI Agent Skills and GitHub repositories. Sentinel monitors code updates via GitHub Webhooks, blocks regressions against your security baseline, and automatically recovers skill status upon clean rescans.

REST Endpoint	Price	Description
`POST /sentinel/register`	$49.00 / 30d	Register a skill/repo for continuous monitoring. Requires passing initial baseline audit (`score >= 0.80`; returns `HTTP 422` if failed). Issues `webhook_secret`.
`POST /sentinel/webhook/{webhook_secret}`	Free	GitHub Webhook receiver. Audits new release tags, blocks regressions, and logs tamper-evident events.
`POST /sentinel/scan`	$0.05–$0.50	Pay-per-call repository audit (`update_rescan`, `deep_scan`, `forensic_audit`).
`GET /sentinel/status/{repo_full_name}`	Free	Query skill security status (`active`, `blocked`, `unregistered`) and last known good version.
`POST /sentinel/renew`	$49.00 / 30d	Extend 30-day monitoring entitlement for a registered skill (owner-verified).
`GET /sentinel/prices`	Free	Returns current price breakdown for Sentinel services.

Key Features of Sentinel:

Idempotent Webhook Processing: Built-in protection against duplicate GitHub delivery retries (X-GitHub-Delivery tracking).
Auto-Recovery: Skills blocked due to security regressions (status: blocked) are automatically restored to active once a clean, policy-compliant release is published.
Strict Owner Verification: Subscription renewals require cryptographic signature match with the registered owner's wallet (owner_payer_ref).
Baseline Policy Gate: Registration requires a passing initial audit (score >= 0.80). Compromised or failing repositories are rejected immediately (HTTP 422) to prevent invalid entitlement setup.

Crypto & Trading Compliance (MCP only)

These tools are exposed on the live MCP server only (no REST routes in webhook_server.py).

MCP Tool	Price	Description
`dcl_evaluate_jailbreak_crypto`	$0.02	Crypto-specific jailbreak & injection detection.
`dcl_evaluate_wallet`	$0.02	Wallet secret guardian.
`dcl_evaluate_trade`	$0.02	Trade decision verifier.
`dcl_evaluate_mev`	$0.03	MEV & market-abuse compliance screen.
`dcl_evaluate_signal`	$0.03	Market signal fabrication screen.
`dcl_evaluate_output_sanitizer`	$0.02	Output sanitizer — final gate.
`dcl_commit`	$0.01	Leibniz Layer crypto commit — seal a decision to the audit chain.

Utility (free, REST only)

Endpoint	Description
`GET /health`	Service status and chain length
`GET /policies`	List of built-in policy names
`GET /chain/status`	Chain integrity, drift mode, drift score
`GET /chain/export`	Full chain export with integrity verification

Example Response

"verdict": "COMMIT", "confidence": 0.95, "reason": "All policy checks passed", "tx_hash": "0x7a8f3b2c...", "chain_index": 42, "input_hash": "0x9d4e1f...", "policy_version": "1.0.0", "timestamp": 1721635200.123, "pipeline_id": "abc123", "drift_mode": "NORMAL", "drift_score": 0.15 }

Verifying the Chain Yourself

You don't have to take the server's word for it. tx_hash is recomputed from the record's own fields, not just linked to the previous row — so anyone can independently confirm a record wasn't edited after the fact, without calling back into this server. See @fronesis-labs/dcl-sdk (TS/JS) or dcl-core (Python) for the free, offline verification libraries.

Metering & Settlement

Every paid call above is metered and settled automatically via the x402 protocol (USDC on Base).

We support two payment models:

Per-Call Micropayments ($0.01 – $0.50) for instant, on-demand evaluations.
30-Day Recurring Entitlements ($49.00) for continuous skill monitoring via DCL Update Sentinel.

This design eliminates API-key provisioning and invoicing overhead. Per-call pricing makes large-scale agent operations practical (an autonomous system can make thousands of evaluation calls a day).

The REST API is x402-gated via fastapi-x402; the MCP server uses paymcp in Mode.X402, paying automatically for x402-aware clients and falling back to a guided payment link for clients without a configured wallet. Both settle to the same wallet, and neither has a bypass path — an unpaid call simply receives no verdict.

License

Apache License 2.0 — see LICENSE.

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

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

DCL Trust Oracle

What It Does

Quick Start

REST API

MCP Server

Tools & Endpoints

Pre-Action Evaluation

Session Management

Post-Action Forensics

Post-Action Scans

DCL Update Sentinel (Continuous Skill Monitoring)

Key Features of Sentinel:

Crypto & Trading Compliance (MCP only)

Utility (free, REST only)

Example Response

Verifying the Chain Yourself

Metering & Settlement

License