Provenance
Enables querying deterministic risk ratings and explanations for tokenized assets on Mantle.
README
PROVENANCE
The AI ratings agency for tokenized assets. Deterministic, on-chain-anchored risk dossiers that humans can read and agents can act on.
Mantle Turing Test 2026 — AI x RWA track
Live: Landing · Dossier Viewer · Docs · Registry on Explorer
Every RWA project at this hackathon built a vault. We built the ratings agency that tells you which vaults are safe.
What it does
PROVENANCE underwrites tokenized assets with a deterministic scoring engine and publishes versioned risk dossiers on-chain on Mantle, so any human or agent can check an asset's risk profile before touching it.
Four assets rated in v1: USDY (Ondo), mETH (Mantle LST), USDe (Ethena), FBTC — each with different risk profiles, different grades, and different reasons.
The Anti-Hallucination Defense
The LLM never produces a score. Ever.
- Scores come from a deterministic rubric: published weights, quantifiable inputs, reproducible output. Same inputs → same score, verifiable by anyone.
- Five dimensions: collateral quality (25%), redemption mechanics (20%), liquidity depth (20%), concentration risk (20%), transparency (15%).
- When data is missing, the engine flags it unknown and redistributes weight. It never silently defaults.
- The methodology hash is anchored on-chain. A rubric change = new version, publicly visible.
- The LLM writes only the narrative layer (plain-English explanation). Every number in the prose is validated against computed values — mismatches trigger regeneration.
Architecture
┌──────────────────────────────────────────────────┐
│ Data Sources │
│ on-chain probes · DEX pools · explorer APIs │
│ sourced docs corpus (every field has a URL) │
└──────────────────┬───────────────────────────────┘
▼
┌──────────────────────────────────────────────────┐
│ Deterministic Rubric Engine │
│ weights.json · score.ts · pure functions │
│ same inputs → same output (tested) │
└──────────────────┬───────────────────────────────┘
▼
┌──────────────────────────────────────────────────┐
│ DossierRegistry (Solidity) │
│ publishDossier() · latest() · history() │
│ methodology hash pins the rubric version │
└──────────────────┬───────────────────────────────┘
▼
┌──────────────────────────────────────────────────┐
│ Consumption Surfaces │
│ MCP server · REST API · Frontend viewer │
│ narratives: LLM-generated, number-validated │
└──────────────────────────────────────────────────┘
Deployed Addresses (Mantle Sepolia — chain 5003)
| Contract | Address |
|---|---|
| DossierRegistry | 0xd1534d20006248f4c2c290F83e6377b4A06037A9 |
| Publisher | 0x093c1F3C6daA784376dF100e361F692DbB33acd8 |
Verification: Sourcify exact match.
Rated Assets (v1)
| Asset | Composite | Grade | Key Risk |
|---|---|---|---|
| USDY | 60.2 | B | Critically thin Mantle DEX liquidity ($5k TVL) |
| mETH | 66.1 | B | 4-day unstaking + issuer-chain correlation |
| USDe | 72.6 | A | Synthetic collateral: basis/funding rate risk |
| FBTC | 76.7 | A | No sourced redemption path; custodian dependency |
Spread: 16.5 points across 2 grade bands — the rubric discriminates.
Quick Start
# Install
npm install
# Run tests (22 tests)
npm test
# Score all assets from cached snapshots (no network)
npm run dev -- score all --from-snapshot
# Live probes + score (needs internet)
npm run dev -- probe all
npm run dev -- score all
# Publish dossiers on-chain (needs .env with keys)
npm run dev -- publish all --live
# Start REST API + frontend
npm run api
# → http://localhost:3000
# Build static site (no API dependency)
npm run build:site
# → dist/site/index.html
# MCP server (for Claude Code / MCP clients)
npm run mcp
Project Structure
contracts/ Solidity — DossierRegistry.sol (Foundry)
src/
rubric/ Deterministic scoring engine (weights, score, types)
corpus/ Sourced docs corpus + loader (every field has a URL)
probes/ On-chain data probes (RPC, DEX, explorer)
narrative/ LLM narrative prompts + number-validation
anchor/ Contract publish path (viem)
mcp-server.ts MCP server (3 tools)
api.ts REST API (node:http)
cli.ts CLI (probe, score, publish)
data/
assets/ Structured docs corpus per asset
snapshots/ Cached probe results (demo resilience)
dossiers/ Canonical dossier JSON
narratives/ Pre-generated + validated narratives
frontend/ Single-page dossier viewer
test/ Vitest tests (rubric, corpus, narrative)
Documentation & Agent Skill
- Docs page:
landing/docs.html— REST, MCP, on-chain reference, methodology. - Agent skill:
skills/provenance-ratings/SKILL.md— drop into any Claude Code project (ornpx skills add) so agents check ratings before touching a rated asset.
MCP Tools
| Tool | Description |
|---|---|
PROVENANCE_LIST |
List all rated assets with composite scores and grades |
PROVENANCE_GET_RATING |
Full risk dossier for a specific asset |
PROVENANCE_EXPLAIN |
Plain-English narrative explanation (number-validated) |
Tech Stack
- Engine: TypeScript, Zod, Vitest
- Chain: Mantle Sepolia (5003), Solidity 0.8.24, Foundry
- Client: viem
- Frontend: vanilla HTML/CSS/JS, editorial dark theme (Syne + Space Grotesk + JetBrains Mono)
- MCP: @modelcontextprotocol/sdk
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。