behavioral-prediction-mcp
AI-powered tools to analyze wallet behaviour prediction,fraud detection and rug pull prediction.
Install / Use
claude mcp add ChainAware -- npx -y github:ChainAware/behavioral-prediction-mcpIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
Development & EngineeringSupported Platforms
Tags
Skill content
View source on GitHub🧠 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/]
LinkedIn: [https://www.linkedin.com/company/chainaware]
Blog: [https://chainaware.ai/blog]
Learn: [https://chainaware.ai/learn]
Examples: [https://github.com/ChainAware/examples]
Featured in: CB Insights Fraud Prevention Market Map for the AI Era — ChainAware recognised as a leading AI-era fraud prevention solution for Web3 (2026).
Listed on: BNB Chain AI Landscape — ChainAware listed by BNB Chain as a key AI project in the ecosystem (2025).
Listed on: BNB Chain Kickstart — Marketing Tools — ChainAware's Growth Agents and Wallet Marketer featured in the BNB Chain Kickstart programme's Marketing Tools section (2025).
Awarded: Google Cloud $250k Grant — ChainAware selected for a $250,000 Google Cloud grant (2025).
Selected for: AWS Fintech Accelerator — ChainAware accepted into the AWS Fintech Accelerator programme (2024).
Listed on: Safary Club Web3 Growth Landscape — Growth Tools — ChainAware featured in the Growth Tools for Web3 Projects category (2024).
<!-- MCP -->mcp-name: io.github.ChainAware/chainaware-behavioral-prediction-mcp
📖 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.
Backtesting verified: 98% Predictive Fraud Detection accuracy · 90.1% Rug Pull Detection accuracy
🏆 ChainAware is featured in the CB Insights Fraud Prevention Market Map for the AI Era — recognised as a leading AI-powered fraud prevention solution for Web3 (2026). 🌐 ChainAware is listed on the BNB Chain AI Landscape — recognised as a key AI project in the BNB Chain ecosystem (2025). 🚀 ChainAware is listed on BNB Chain Kickstart — Marketing Tools — Growth Agents and Wallet Marketer featured in BNB Chain's official programme for ecosystem growth (2025). 💰 ChainAware received a $250k Google Cloud Grant — selected by Google Cloud for infrastructure and AI development (2025). ☁️ ChainAware was accepted into the AWS Fintech Accelerator — selected by AWS for its Fintech Accelerator programme (2024). 🌱 ChainAware is featured in the Safary Club Web3 Growth Landscape — Growth Tools — listed in the Growth Tools for Web3 Projects category (2024).
⚙️ 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% backtesting 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", // e.g. “Success” or error description
"walletAddress": "string", // blockchain wallet address that was analyzed
"chain": "string", // blockchain network identifier (e.g. ETH, BNB,POLYGON,TON,BASE, TRON, HAQQ)
"status": "string", // classification result (e.g. “Fraud” | “Not Fraud” | “New Address”)
"probabilityFraud": "0.00–1.00", // decimal fraud probability score (string to preserve precision)
"token": "string | null", // optional token associated with the check (may be null)
"lastChecked": "ISO-8601 timestamp", // last time the wallet risk analysis was executed
"forensic_details": {
"cybercrime": "string", // indicator score for cybercrime activity
"money_laundering": "string", // indicator score for money laundering activity
"number_of_malicious_contracts_created": "string", // number of malicious contracts deployed by this wallet
"gas_abuse": "string", // gas abuse indicator
"financial_crime": "string", // financial crime indicator
"darkweb_transactions": "string", // interaction with darkweb-linked wallets
"reinit": "string", // reinitialization exploit indicator
"phishing_activities": "string", // phishing activity indicator
"fake_kyc": "string", // fake KYC related activity
"blacklist_doubt": "string", // suspected blacklist association
"fake_standard_interface": "string", // fake ERC interface indicator
"data_source": "string", // source of forensic intelligence (may be empty)
"stealing_attack": "string", // stealing attack indicator
"blackmail_activities": "string", // blackmail activity indicator
"sanctioned": "string", // sanction exposure indicator
"malicious_mining_activities": "string", // malicious mining indicator
"mixer": "string", // interaction with mixing services
"fake_token": "string", // fake token creation or usage indicator
"honeypot_related_address": "string" // interaction with honeypot-related addresses
},
"checked_times": 0, // integer — number of times this wallet has been analyzed
"createdAt": "ISO-8601 timestamp", // record creation timestamp
"updatedAt": "ISO-8601 timestamp", // record last update timestamp
"sanctionData": [
{
"category": "string | null", // sanction category (may be null)
"name": "string | null", // sanction list name
"description": "string | null", // sanction description
"url": "string | null", // source URL for sanction information
"isSanctioned": false, // boolean — whether the wallet is officially sanctioned
"createdAt": "ISO-8601 timestamp", // sanction record creation timestamp
"updatedAt": "ISO-8601 timestamp" // sanction record last update timestamp
}
]
}
Error cases:
• `403 Unauthorized` → invalid `apiKey`
• `400 Bad Request` → malformed `network` or `walletAddress`
• `500 Internal Server Error` → temporary downstream failure
2. Batch Predictive Fraud Detection Tool
ID: predictive_fraud_batch
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 in batch. Schedule a batch fraud calculation job for a list of wallet addresses. Use this when the user provides a CSV or list of addresses to analyse. Returns a job_id and signature immediately — report the job_id to the user and store both job_id and signature in context, they are required for all follow-up calls. Do NOT poll or wait for results after scheduling.
➡️ Example Use Cases:
• Run fraud batch for this list of addresses on ETH network ?
• What is the fraudulent status of this addresses on BNB ?
Inputs:
| Name | Type | Required | Description |
| --------------- | ----------------- | -------- | ------------------------------------------------------------------------- |
| apiKey | string | ✅ | API key for authentication |
| network | string | ✅ | Blockchain network (ETH, BNB,POLYGON,TON,BASE, TRON, HAQQ) |
| addresses | array[objects] | ✅ | The list ofwallet address to evaluate |
Outputs (JSON):
{
"message": "Job scheduled successfully.",
"job_id": "0fc5897a-ad64-4f21-88b5-1274d1cfec46",
"signature": "260866090d88bf61bdfb54f0533fe876bfd8ded7339691c50ada9de59a48124a",
"total_items": 5,
"chunks_enqueued": 1,
"status": "pending"
}
Error cases:
• `403 Unauthorized` → invalid `apiKey`
• `400 Bad Request` → malformed `network` or `walletAddress`
• `500 Internal Server Error` → temporary downstream failure
3. 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
Truncated for display — read the full file on GitHub.
Related Skills
Agent-Reach
84.4kGive your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
headroom
73.4kCompress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
ruflo
73.0k🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
career-ops
72.4kOpen-source AI job search: scan job portals, evaluate listings into a structured A-H report with a global 1-5 score, tailor your CV, track applications — runs locally in your AI coding CLI (Claude Code, Codex, OpenCode, Antigravity…)
Security Score
Audited on Aug 11, 2026
