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

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

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fronesislabs.comSecurity1Updated 2w ago

About 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

  1. 01Fast, strict, safety, and quality policy checks
  2. 02Detect jailbreaks and prompt injection
  3. 03Scan for secrets and PII
  4. 04Record decisions in a tamper-evident hash chain
  5. 05Decode audit records for post-action forensics
  6. 06Crypto and trading compliance checks

Use cases

  1. 01Screen agent outputs before they reach users or systems
  2. 02Audit decisions after an agent action
  3. 03Track drift in long-running agent sessions
  4. 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.

Endpointhttps://mcp.fronesislabs.com/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

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
ParameterTypeRequiredDescription
agent_idstringYesIdentifier of the agent that produced the response.
responsestringYesThe agent or LLM response text to audit.

Details

From the project README

DCL Trust Oracle

Python 3.11+ MCP License: Apache 2.0 dcl-webhook MCP server Smithery

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 EndpointMCP ToolPriceDescription
POST /evaluate/fastdcl_evaluate_fast$0.01Fast policy check for low-risk outputs. Returns tamper-evident tx_hash.
POST /evaluate/strictdcl_evaluate_strict$0.05Deep analysis for high-stakes outputs with higher confidence thresholds.
POST /evaluate/jailbreakdcl_evaluate_jailbreak$0.02Instruction adherence check — detects prompt injection patterns and role-hijacking attempts.
POST /evaluate/safetydcl_evaluate_safety$0.01Baseline screening for known harmful text patterns. Optimized for high throughput.
POST /evaluate/qualitydcl_evaluate_quality$0.03Content quality & drift check — evaluates format adherence and contextual drift.
POST /evaluate/batchdcl_evaluate_batch$0.10Bulk processing — up to 200 items per MCP call (REST default max_items: 20). Cost-effective for multi-turn history.
Session Management
REST EndpointMCP ToolPriceDescription
POST /pipeline/startdcl_pipeline_start$0.05Initializes 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 EndpointMCP ToolPriceDescription
GET /audit/{tx_hash}dcl_audit_decode$0.10Basic post-action audit — returns verdict, confidence, agent_id, reason by tx_hash.
GET /audit/{tx_hash}/deepdcl_audit_decode_deep$0.50Deep forensic audit — includes drift context, tamper-evidence indices, environmental metadata.
Post-Action Scans
REST EndpointMCP ToolPriceDescription
POST /evaluate/secretsdcl_evaluate_secrets$0.02Secret & credential leak scan.
POST /evaluate/piidcl_evaluate_pii$0.02PII 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 EndpointPriceDescription
POST /sentinel/register$49.00 / 30dRegister 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}FreeGitHub Webhook receiver. Audits new release tags, blocks regressions, and logs tamper-evident events.
POST /sentinel/scan$0.05–$0.50Pay-per-call repository audit (update_rescan, deep_scan, forensic_audit).
GET /sentinel/status/{repo_full_name}FreeQuery skill security status (active, blocked, unregistered) and last known good version.
POST /sentinel/renew$49.00 / 30dExtend 30-day monitoring entitlement for a registered skill (owner-verified).
GET /sentinel/pricesFreeReturns 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 ToolPriceDescription
dcl_evaluate_jailbreak_crypto$0.02Crypto-specific jailbreak & injection detection.
dcl_evaluate_wallet$0.02Wallet secret guardian.
dcl_evaluate_trade$0.02Trade decision verifier.
dcl_evaluate_mev$0.03MEV & market-abuse compliance screen.
dcl_evaluate_signal$0.03Market signal fabrication screen.
dcl_evaluate_output_sanitizer$0.02Output sanitizer — final gate.
dcl_commit$0.01Leibniz Layer crypto commit — seal a decision to the audit chain.
Utility (free, REST only)
EndpointDescription
GET /healthService status and chain length
GET /policiesList of built-in policy names
GET /chain/statusChain integrity, drift mode, drift score
GET /chain/exportFull 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.

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