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BaseSentinel

by 0xConsole
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Python
Hybrid

BaseSentinel — AI DeFi Risk Monitor for Base L2

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.

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

Related MCP server: defi-yield-scanner-mcp

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

This server cannot be installed

A
license - permissive license
-
quality - not tested
C
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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