DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)
RemoteAbout DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)
DCL Trust Oracle evaluates LLM and agent outputs against policy and records decisions in a tamper-evident hash chain. It is for teams auditing agent behavior before or after consequential actions, with MCP and REST interfaces and x402 payments.
Key features
- 01Fast, strict, safety, and quality policy checks
- 02Detect jailbreaks and prompt injection
- 03Scan for secrets and PII
- 04Record decisions in a tamper-evident hash chain
- 05Decode audit records for post-action forensics
- 06Crypto and trading compliance checks
Use cases
- 01Screen agent outputs before they reach users or systems
- 02Audit decisions after an agent action
- 03Track drift in long-running agent sessions
- 04Monitor GitHub skill or repository updates
MCP support
The MCP server exposes evaluation, audit, session, and crypto-compliance tools to agents. Paid calls use x402 settlement in USDC on Base.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://mcp.fronesislabs.com/mcpclaude mcp add --transport http com-fronesislabs-dcl-trust-oracle https://mcp.fronesislabs.com/mcp{"mcpServers":{"com-fronesislabs-dcl-trust-oracle":{"type":"http","url":"https://mcp.fronesislabs.com/mcp"}}}Open in Cursor{"servers":{"com-fronesislabs-dcl-trust-oracle":{"type":"http","url":"https://mcp.fronesislabs.com/mcp"}}}Open in VS Code{"mcpServers":{"com-fronesislabs-dcl-trust-oracle":{"type":"http","url":"https://mcp.fronesislabs.com/mcp"}}}{"mcpServers":{"com-fronesislabs-dcl-trust-oracle":{"serverUrl":"https://mcp.fronesislabs.com/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
18| Parameter | Type | Required | Description |
|---|---|---|---|
| agent_id | string | Yes | Identifier of the agent that produced the response. |
| response | string | Yes | The agent or LLM response text to audit. |
Details
From the project READMEDCL 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 athttps://mcp.fronesislabs.com/mcp(streamable-http). Also listed on Smithery (remote URL → same production endpoint; seesmithery.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-Deliverytracking). - Auto-Recovery: Skills blocked due to security regressions (
status: blocked) are automatically restored toactiveonce 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.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.