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Dcl Webhook

Fronesis-Labs
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Deterministic AI audit layer: evaluates LLM/agent outputs against configurable policies (jailbreak, safety, quality, wallet, trade, MEV compliance) and writes every verdict to a tamper-evident SHA-256 hash chain โ€” metadata only, never raw content. 17 tools including a full crypto-trading compliance suite. Pay-per-call via x402 (USDC on Base), from $0.01. mcp.fronesislabs.com/mcp

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ€” or use 1-click editor setup below.

Manual Client & Custom JSON ConfigExpand JSON โ–พ

Install Config Generator

claude_desktop_config.json
{
  "mcpServers": {
    "fronesis-labs-dcl-webhook": {
      "command": "npx",
      "args": [
        "-y",
        "https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/"
      ]
    }
  }
}

๐Ÿ’ก Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)

Documentation Overview

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 โ€” 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 โ€” a deterministic, cryptographically verifiable record of what happened and when. Edit any past entry and the entire chain invalidates. No one โ€” not even Fronesis Labs โ€” 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 โ€” never raw content โ€” enabling verifiable, post-action forensic analysis across distributed AI agents.

Available two ways:

  • REST API (webhook_server.py) โ€” direct HTTP integration.
  • MCP Server (mcp_server.py) โ€” native Model Context Protocol integration for AI agents, hosted on Smithery.

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

Quick Start

REST API

Terminal
pip install -r requirements.txt
python webhook_server.py

Server runs on http://localhost:8080

MCP Server

Terminal
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 โ€” 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 โ€” evaluates format adherence and contextual drift.
POST /evaluate/batchdcl_evaluate_batch$0.10Bulk processing โ€” up to 20 items per transaction. 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 โ€” returns verdict, confidence, agent_id, reason by tx_hash.
GET /audit/{tx_hash}/deepdcl_audit_decode_deep$0.50Deep forensic audit โ€” includes drift context, tamper-evidence indices, environmental metadata.

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

config.json
{
  "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 โ€” 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 per request, via the x402 protocol (USDC on Base, Avalanche, or IoTeX) โ€” no subscription, no API-key provisioning, no invoicing overhead. This is what makes per-call pricing practical at agent scale (an autonomous system can make thousands of evaluation calls a day). The REST API is x402-gated via fastapi-x402; the MCP server via paymcp in Mode.AUTO, which pays automatically for x402-aware clients and falls back to a guided payment link for clients without a wallet configured. Both settle to the same wallet, and neither has a bypass path โ€” an unpaid call simply gets no verdict.

License

Apache License 2.0 โ€” see LICENSE.

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Frequently Asked Questions about Dcl Webhook

How do I install the Fronesis-Labs/dcl-webhook MCP server?

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "dcl-webhook": { "command": "npx", "args": ["-y", "Fronesis-Labs/dcl-webhook"] } }

What does Fronesis-Labs/dcl-webhook do?

Deterministic AI audit layer: evaluates LLM/agent outputs against configurable policies (jailbreak, safety, quality, wallet, trade, MEV compliance) and writes every verdict to a tamper-evident SHA-256 hash chain โ€” metadata only, never raw content. 17 tools including a full crypto-trading compliance suite. Pay-per-call via x402 (USDC on Base), from $0.01. mcp.fronesislabs.com/mcp

Is the Fronesis-Labs/dcl-webhook MCP server free to use?

Yes. Fronesis-Labs/dcl-webhook is listed on AllMCPs as a free, open Model Context Protocol server you can install into Claude Desktop, Cursor, or any MCP-compatible client.

Technical Specs & Signals

More technical detailsExpand โ–พ
TransportSSE (Remote)
RuntimeNode.js
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GitHub stars1
44Quality signal: Fair ยท 44/100How this signal is calculated โ–พ
Server availabilityNot measured

Not scored for repo-hosted servers โ€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools24/30
Adoption1/15
Community engagement0/10

A guidance signal from public completeness & health data โ€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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