Behavioural Prediction MCP Server

🧠 Behavioural Prediction MCP Server

The Behavioural Prediction MCP Server offers AI - powered tools for wallet behavior prediction, fraud detection, and rug pull prediction, safeguarding DeFi users and providing trustworthiness scores.

🚀 Quick Start

The Behavioural Prediction MCP Server is a public - facing tool with a private backend. It can be accessed by request with an API key. Here are the basic details:

• MCP Server Name: Behavioural Prediction MCP
• Category: Web3 / Security / DeFi Analytics
• Status: Public tools – Private backend
• Access: By request (API key)
• Server URL: [https://prediction.mcp.chainaware.ai/]
• Repository: [https://github.com/ChainAware/behavioral - prediction - mcp]

✨ Features

The server provides the following AI - powered tools:

• Analyze wallet behavior prediction.
• Conduct fraud detection.
• Perform rug pull prediction.
• Developers and platforms can integrate these tools via the MCP protocol to protect DeFi users, monitor liquidity risks, and score wallet or contract trustworthiness.

📦 Installation

There is no specific installation guide provided in the original README.

💻 Usage Examples

Node.js Example

import { MCPClient } from "mcp - client";

const client = new MCPClient("https://prediction.mcp.chainaware.ai/");

const result = await client.call("predictive_rug_pull", {
  apiKey: "your_api_key",
  network: "BNB",
  walletAddress: "0x1234..."
});

console.log(result);

Python Example

from mcp_client import MCPClient

client = MCPClient("https://prediction.mcp.chainaware.ai/")

res = client.call("chat", {"query": "What is the rug pull risk of 0x1234?"})
print(res)

📚 Documentation

Available Tools

1. Predictive Fraud Detection Tool

• ID: predictive_fraud
• Description: This AI - powered algorithm forecasts the likelihood of fraudulent activity on a given wallet address before it happens (≈98% accuracy) and performs AML/Anti - Money - Laundering checks. Use it when your user wants a risk assessment or early - warning on a blockchain address.
• Example Use Cases:
  • Is it safe to interact with vitalik.eth?
  • What is the fraudulent status of this address?
  • Is my new wallet at risk of being used for fraud?
• Inputs: | Property | Details | |----------|---------| | apiKey | string, required for authentication | | network | string, required, Blockchain network (ETH, BNB,POLYGON,TON,BASE, TRON, HAQQ) | | walletAddress | string, required, The wallet address to evaluate |
• Outputs (JSON):

{
    "message": "string",              // Human - readable status message
    "walletAddress": "string",        // hex address 
    "status": "Fraud",                // Fraudelent status (Fraud,Not Fraud,New Address)
    "probabilityFraud": "0.00–1.00",  // Decimal probability
    "token": "string",                //
    "lastChecked": "ISO - 8601 timestamp",
    "forensic_details": {             // Deep forensic breakdown
    /* ...other metrics... */
    },
    "createdAt": "ISO - 8601 timestamp", 
    "updatedAt": "ISO - 8601 timestamp"
}

• Error cases:
  • 403 Unauthorized → invalid apiKey
  • 400 Bad Request → malformed network or walletAddress
  • 500 Internal Server Error → temporary downstream failure

2. Predictive Behaviour Analysis Tool

• ID: predictive_behaviour
• Description: This AI - driven engine projects what a wallet address intentions or what address is likely to do next, profiles its past on - chain history, and recommends personalized actions.
• Example Use Cases:
  • “What will this address do next?”
  • “Is the user high - risk or experienced?”
  • “Recommend the best DeFi strategies for 0x1234... on ETH network.”
• Inputs: | Property | Details | |----------|---------| | apiKey | string, required for authentication | | network | string, required, Blockchain network (ETH, BNB,BASE,HAQQ) | | walletAddress | string, required, The wallet address to evaluate |
• Outputs (JSON):

{
    "message":           "string",                    // e.g. “Success” or error text  
    "walletAddress":     "string",                    // echoed input  
    "status":            "string",                    // Fraudelent status (Fraud,Not Fraud,New Address)  
    "probabilityFraud":  "0.00–1.00",                 // decimal fraud score  
    "lastChecked":       "ISO - 8601 timestamp",        // e.g. “2025 - 01 - 03T16:19:13.000Z”  
    "forensic_details":  { /* dict of forensic metrics */ },  
    "categories":        [ { "Category":"string", "Count":int }, … ],  
    "riskProfile":       [ { "Category":"string", "Balance_age":float }, … ],  
    "segmentInfo":       "JSON - string of segment counts",  
    "experience":        { "Type":"Experience", "Value":int },  
    "intention":         {                              
    "Type":"Intentions",  
    "Value": { "Prob_Trade":"High", "Prob_Stake":"Medium", … }  
    },  
    "protocols":         [ { "Protocol":"string","Count":int }, … ],  
    "recommendation":    { "Type":"Recommendation", "Value":[ "string", … ] },  
    "createdAt":         "ISO - 8601 timestamp",  
    "updatedAt":         "ISO - 8601 timestamp"  
}

• Error cases:
  • 403 Unauthorized → invalid apiKey
  • 400 Bad Request → malformed network or walletAddress
  • 500 Internal Server Error → temporary downstream failure

3. Predictive Rug - Pull Detection Tool

• ID: predictive_rug_pull
• Description: This AI - powered engine forecasts which liquidity pools or contracts are likely to perform a “rug pull” in the future. Use it when you need to warn users before they deposit into risky pools or to monitor smart - contract security on - chain.
• Example Use Cases:
  • “Will this new DeFi pool rug - pull if I stake my assets?”
  • “Monitor my LP position for potential future exploits.”
• Inputs: | Property | Details | |----------|---------| | apiKey | string, required for authentication | | network | string, required, Blockchain network (ETH, BNB, BASE, HAQQ) | | walletAddress | string, required, Smart contract or liquidity pool address |
• Outputs (JSON):

{
  "message": "Success",
  "contractAddress": "0x1234...",
  "status": "Fraud",
  "probabilityFraud": 0.87,
  "lastChecked": "2025-10-25T12:45:00Z",
  "forensic_details": { /* dict of on - chain metrics */ }, 
  "createdAt": "2025-10-25T12:45:00Z",
  "updatedAt": "2025-10-25T12:45:00Z"
}

• Error cases:
  • 403 Unauthorized → invalid apiKey
  • 400 Bad Request → malformed network or walletAddress
  • 500 Internal Server Error → temporary downstream failure

Service Configuration

{
  "type": "http",
  "config": {
    "mcpServers": {
      "behavioural_prediction_mcp": {
        "type": "http",
        "url": "https://prediction.mcp.chainaware.ai/sse",
        "description": "The Behavioural Prediction MCP Server provides AI - powered tools to analyze wallet behaviour prediction,fraud detection and rug pull prediction.",
        "headers":{
          "x - api - key":""
        },
        "params":{
          "walletAddress":"",
          "network":""
        },
        "auth": {
          "type": "api_key",
          "header": "X - API - Key"
        }
      }
    }
  }
}

Integration Notes

• Compatible with all MCP clients (Node, Python, Browser).
• Uses Server - Sent Events (SSE) for real - time responses.
• JSON schemas match MCP spec.
• Rate limits may apply.
• API key required for production endpoints.

Access Policy

The MCP server requires an API key for production usage. To request access, you can subscribe to listed available plans via: https://chainaware.ai/pricing

📄 License

The client examples are under the MIT license. The server implementation and backend logic are proprietary and remain private.