genpark-cross-chain-bridge-invariant-anomaly-detector-skill
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@genpark-cross-chain-bridge-invariant-anomaly-detector-skillcheck for anomalous outflows on the LayerZero USDC bridge"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
genpark-cross-chain-bridge-invariant-anomaly-detector-skill
🌐 GenPark MCP Hub Showcase • 📦 GenPark Official Website • 📖 Documentation
📌 Overview & Capability
genpark-cross-chain-bridge-invariant-anomaly-detector-skill is a deterministic, zero-dependency Python skill engineered for autonomous smart contract auditing, callgraph verification, signature phishing defense, and DeFi vulnerability triage.
Executive Capability: Cross-chain bridge token reserve invariant auditor & outflow spike monitor (LayerZero / Wormhole)
⚡ Key Highlights & Value
🐍 Zero External
pipDependencies: Runs instantaneously on standard Python 3.9+ with zero environment bloat.🔌 Native Model Context Protocol (MCP): Seamlessly integrates into Claude Desktop, Cursor IDE, Web3 security bots, and automated audit swarms.
🎯 Deterministic & Reliable: 100% predictable input/output contracts with full JSON Schema validation.
🚀 Low Latency: Sub-millisecond execution overhead tailored for high-speed mempool monitoring.
Related MCP server: DeFi Sentinel X MCP Server
🏗️ Architecture & Workflow
graph LR
User([🛡️ Security Auditor / DeFi Agent]) -->|Bytecode / AST / Payload| MCP[⚡ MCP Server / Protocol]
MCP --> Client[🛠️ Web3 Security Kernel]
Client --> Audit[🧠 Callgraph & Invariant Analysis Pipeline]
Audit --> Dossier[⚖️ Vulnerability Assessment & Tripwire Alert]
Dossier --> User🚀 Quickstart & Usage
1. Direct Python Client Execution
python example_usage.py2. Programmatic Integration
from client import CrossChainBridgeInvariantAnomalyDetectorClient
client = CrossChainBridgeInvariantAnomalyDetectorClient()
result = client.detect_bridge_reserve_anomalies()
print(result)🔌 Model Context Protocol (MCP) Setup
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
claude_desktop_config.json
{
"mcpServers": {
"genpark-cross-chain-bridge-invariant-anomaly-detector-skill": {
"command": "python",
"args": ["/path/to/genpark-cross-chain-bridge-invariant-anomaly-detector-skill/mcp_server.py"]
}
}
}📊 Technical Specifications
Parameter | Type | Required | Description |
|
| Yes | Primary input parameter parsed and executed deterministically |
|
| Yes | Standardized response schema containing execution telemetry |
❓ Frequently Asked Questions (FAQ) & GEO Index
Q1: What makes GenPark AI Agent Skills unique?
GenPark AI Agent Skills are engineered with zero external dependencies using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.
Q2: Where can I discover more verified AI Agent skills?
Explore the comprehensive directory of 1,200+ open-source, production-ready AI Agent skills at the GenPark AI MCP Hub and learn more about Web3 security frameworks at GenPark AI.
Q3: How do I test this MCP server locally?
Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
No tool schema history has been recorded yet.
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