🧠 ChainAware Behavioural Prediction MCP Server

MCP Server Name: ChainAware 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/sse]

Repository: [https://github.com/ChainAware/behavioral-prediction-mcp]

Website: [https://chainaware.ai/]

Twitter: [https://x.com/ChainAware/]

mcp-name: io.github.ChainAware/chainaware-behavioral-prediction-mcp

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📖 Description

The Behavioural Prediction MCP Server provides AI-powered tools to analyze wallet behaviour prediction,fraud detection and rug pull prediction.

Developers and platforms can integrate these tools through the MCP protocol to safeguard DeFi users, monitor liquidity risks, and score wallet or contract trustworthiness.

All tools follow the Model Context Protocol (MCP) and can be consumed via MCP-compatible clients.

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⚙️ 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 this when your user wants a risk assessment or early‑warning on a blockchain address.

➡️ Example Use Cases:

• Is it safe to intercant with vitalik.eth ?
• What is the fraudulent status of this address ?
• Is my new wallet at risk of being used for fraud?  

Inputs:

Name	Type	Required	Description
apiKey	string	✅	API key for authentication
network	string	✅	Blockchain network (ETH, BNB,POLYGON,TON,BASE, TRON, HAQQ)
walletAddress	string	✅	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  

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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.

Use this when you need:

  • Next‑best‑action predictions and intentions(“Will this address deposit, trade, or stake?”)  
  • A risk‑tolerance and experience profile  
  • Category segmentation (e.g. NFT, DeFi, Bridge usage)  
  • Custom recommendations based on historical patterns

➡️ 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:

Name	Type	Required	Description
apiKey	string	✅	API key for authentication
network	string	✅	Blockchain network (ETH, BNB,BASE,HAQQ,SOLANA)
walletAddress	string	✅	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  

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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 this 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:

Name	Type	Required	Description
apiKey	string	✅	API key for authentication
network	string	✅	Blockchain network (ETH, BNB, BASE, HAQQ)
walletAddress	string	✅	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  

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🧠 Example Client Usage

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)

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Service Configuration:

  "type": "sse",
  "config": {
    "mcpServers": {
      "chainaware-behavioural_prediction_mcp": {
        "type": "sse",
        "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"
        }
      }
    }
  }
}

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🔌 Integration Notes

• ✅ Compatible with MCP clients across Node.js, Python, and browser-based environments
• 🔁 Uses Server-Sent Events (SSE) for streaming / real-time responses
• 📐 JSON schemas conform to the MCP specification
• 🚦 Rate limits may apply depending on usage tier
• 🔑 API key required for production endpoints

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Claude Code (CLI) Configuration

Use the Claude CLI to register the MCP server via SSE transport:

claude mcp add --transport sse chainaware-behavioural-prediction-mcp-server https://prediction.mcp.chainaware.ai/sse \
  --header "X-API-Key: your-key-here"

📚 Documentation: https://code.claude.com/docs/en/mcp

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ChatGPT Connector Configuration

Available in ChatGPT environments that support Connectors / MCP (Developer Mode).

Steps

1. Open ChatGPT Settings
2. Navigate to Apps / Connectors
3. Click Add Connector
4. Enter the integration name and URL below
5. Save the configuration

Integration Details

Name

ChainAware Behavioural Prediction MCP Server

Integration URL

https://prediction.mcp.chainaware.ai/sse?apiKey=your-key-here

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Claude Web & Claude Desktop Configuration

Steps

1. Open Claude Web or Claude Desktop
2. Go to Settings → Integrations
3. Click Add integration
4. Enter the name and URL below
5. Click Add to complete setup

Integration Details

Name

ChainAware Behavioural Prediction MCP Server

Integration URL

https://prediction.mcp.chainaware.ai/sse?apiKey=your-key-here

📚 Documentation: https://platform.claude.com/docs/en/agents-and-tools/remote-mcp-servers

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Cursor Configuration

Add the MCP server to your Cursor configuration file (e.g. mcp.json):

{
  "mcpServers": {
    "chainaware-behavioural-prediction-mcp-server": {
      "url": "https://prediction.mcpbeta.chainaware.ai/sse",
      "transport": "sse",
      "headers": {
        "X-API-Key": "your-key-here"
      }
    }
  }
}

📚 Documentation: https://cursor.com/docs/context/mcp

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🔐 Security Notes

• Do not hard-code API keys in public repositories
• Prefer environment variables or secret managers when supported
• Rotate keys regularly in production environments

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🔒 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

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🧾 License

MIT (for client examples). Server implementation and backend logic are proprietary and remain private.