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

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

作者：Fronesis-Labs版本 2.3.1i★ –i在云端运行

注册名称com.fronesislabs/dcl-trust-oracle

安装

概览

作者提供的说明（英文原文）

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

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在云端运行
传输方式: Streamable HTTP
分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在作者的服务器上运行：你向它提出的内容会经过他们的服务器。
注册中心没有列出需要填写的密钥。

你需要

在云端运行无需安装任何东西，通过互联网连接

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

适用于网页版、桌面版和手机版 Claude：

打开 Settings → Connectors
点击 Add custom connector
粘贴下面的地址，然后点击 Add
如果要求登录，请登录作者的服务

https://mcp.fronesislabs.com/mcp

在终端中运行这条命令

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

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

ChatGPT 只能连接互联网上的服务器：

打开网页版 ChatGPT → Settings → Connectors (Apps)
点击 Create 或 Add（部分套餐需要先在 Advanced 中开启 Developer mode）
粘贴下面的地址，起个名字，然后保存
如果要求登录，请登录作者的服务

https://mcp.fronesislabs.com/mcp

菜单名称可能因套餐和版本不同而略有差异。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

我们还没有读取这个服务器的工具列表；我们会轮流检查各个端点。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过域名 fronesislabs.com 验证（注册中心会核实域名所有权）
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
… 尚未检查
端点有响应
尚未检查；我们会轮流检查各个端点
… 尚未检查
使用 OAuth 登录
尚未检查；我们会轮流检查各个端点
… 尚未检查
工具描述中没有隐藏指令
尚未检查；我们会在检查端点时读取工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 7264 个服务器的 0%
发布到注册中心的时间比此分类中其他 7264 个服务器的 30% 更新
基础检查结果等于或优于此分类中其他 7264 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

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

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在作者的云端运行。通过互联网连接，无需安装，但你发送的内容会经过作者的服务器。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（2.3.1）于3个月前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接，所以接受远程 MCP 服务器的应用也可以使用，例如开启开发者模式的 ChatGPT。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT昨天更新

安装

Codebase Memory

作者：DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT21天前更新

安装

Exa

作者：exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT上个月更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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.