BaseSentinel

Unlike generic DeFi monitors, BaseSentinel uses MCP tool protocol for AI agent-native integration and focuses exclusively on Base L2 ecosystem protocols with real-time on-chain risk scoring.

Live demo: https://base-sentinel-agent.vercel.app Repo: https://github.com/0xConsole/base-sentinel-agent

Built for the Orion Agents Builder Hackathon — an AI agent for the Base ecosystem that monitors DeFi protocol risk in real time.

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What it does

BaseSentinel continuously evaluates the health of Base-native protocols (Aerodrome, Moonwell, Seamless, Baseline, Aave V3) using statistical anomaly detection. Every monitoring capability is exposed as an MCP (Model Context Protocol) tool, so an AI agent can integrate and call them natively — the agent gets a risk report, detects anomalies, and raises alerts without a human in the loop.

Statistical anomaly detection

Detector	Threshold	What it catches
Z-score	> 3σ	TVL / volume far from rolling mean
Velocity	> 15%	Single-step rate-of-change spike
Liquidity drain	> 3σ on returns	Coordinated withdrawal pattern
TVL risk score	0-100 composite	Weighted liquidity + volume + reserve risk

MCP Tool Registry

Tool	Description
check_pool_health	TVL, volume, reserve, 0-100 risk score, status
detect_anomalies	z-score, velocity, liquidity drain across protocols
generate_risk_report	Per-protocol + ecosystem-wide risk report
monitor_base_protocol	Block-level monitoring with health deltas
alert_on_threshold	Threshold-driven alert generation

API Endpoints

Endpoint	Method	Description
/	GET	Dark-themed dashboard
/api/health	GET	Service + Base RPC status
/api/agent/status	GET	Agent config + MCP tool inventory
/api/demo	GET/POST	Full monitoring cycle (the demo flow)
/api/tools/check_pool_health	GET	MCP tool: check pool health
/api/tools/detect_anomalies	GET	MCP tool: detect anomalies
/api/tools/generate_risk_report	GET	MCP tool: risk report
/api/tools/monitor_base_protocol	GET	MCP tool: monitor protocol
/api/tools/alert_on_threshold	GET	MCP tool: alert evaluation
/api/mcp/tools	GET	MCP tools/list (JSON Schema)
/api/mcp/call	POST	MCP tools/call ({name, arguments})

Quick start (local)

git clone https://github.com/0xConsole/base-sentinel-agent.git
cd base-sentinel-agent
pip install -r requirements.txt
uvicorn app.main:app --reload
# open http://localhost:8000

Demo flow

Click "Run Monitoring Cycle" on the dashboard, or call the endpoint:

curl https://base-sentinel-agent.vercel.app/api/demo | jq .summary

This runs the full autonomous pipeline: monitor_base_protocol → detect_anomalies → generate_risk_report → alert_on_threshold and returns the ecosystem risk score, anomaly count, and active alerts.

Call an MCP tool (agent-native)

# List tools (MCP tools/list)
curl https://base-sentinel-agent.vercel.app/api/mcp/tools | jq .

# Call a tool (MCP tools/call)
curl -X POST https://base-sentinel-agent.vercel.app/api/mcp/call \
  -H 'Content-Type: application/json' \
  -d '{"name":"detect_anomalies","arguments":{"protocol_name":"all"}}' | jq .

Architecture

┌─────────────────────────────────────────────────┐
│  Dashboard (static/index.html — dark theme)      │
│  Real-time fetch · 30s auto-refresh · risk gauge │
└────────────────────┬────────────────────────────┘
                     │ fetch /api/*
┌────────────────────▼────────────────────────────┐
│  FastAPI app (app/main.py)                        │
│  Routes: /, /api/health, /api/demo, /api/agent/*  │
│         /api/tools/*, /api/mcp/*                  │
└────────────────────┬────────────────────────────┘
                     │
┌────────────────────▼────────────────────────────┐
│  Agent (app/agent.py) — MCP Tool Registry         │
│  • check_pool_health   • monitor_base_protocol    │
│  • detect_anomalies    • alert_on_threshold       │
│  • generate_risk_report                           │
│  Statistical: z-score >3σ, velocity >15%, drain   │
└────────────────────┬────────────────────────────┘
                     │ eth_blockNumber RPC
┌────────────────────▼────────────────────────────┐
│  Base L2 RPC (mainnet.base.org → sepolia → mock) │
│  Protocols: Aerodrome, Moonwell, Seamless,       │
│             Baseline, Aave V3                     │
└──────────────────────────────────────────────────┘

Tech stack

• Backend: FastAPI + Pydantic (Python)
• Chain data: Base L2 public RPC (free), deterministic mock fallback
• Frontend: Single-file dark dashboard (vanilla HTML/CSS/JS)
• Deploy: Vercel serverless free tier (@vercel/python + @vercel/static)
• MCP: JSON Schema tool definitions, /api/mcp/tools + /api/mcp/call

What's real vs mocked

Component	Status
FastAPI backend + 5 MCP tools	Real — fully implemented
Statistical anomaly detection	Real — z-score, velocity, drain
Base L2 RPC integration	Real — probes mainnet.base.org; falls back to mock telemetry if RPC unreachable
Protocol TVL series	Mock when RPC offline (deterministic, preserves statistical signal shape) — real contract addresses used as identity anchors
Dashboard + risk gauge	Real — live fetch + auto-refresh
Vercel deploy	Real — base-sentinel-agent.vercel.app

Orion Agents Builder Hackathon

• Hackathon: orionagents.org/hackathon
• Track: AI agent on Base ecosystem
• Prize: $5K, 7 winners, Sep 2 deadline
• Repo: github.com/0xConsole/base-sentinel-agent
• Live: base-sentinel-agent.vercel.app

License

MIT