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
Our assessment of behavioral-prediction-mcp
behavioral-prediction-mcp scores 72/100 on our quality scale, 3769th of 4,613 Development & Engineering skills we index.
Its MCP Server is 79 KB long, well organised into 43 sections with 25 code examples: long enough that it reads more like full documentation than a focused instruction file, which agents can find harder to follow.
It has 10 GitHub stars, so there is little community track record yet; judge it on its content.
Maintenance, license and trust
- The repository was last updated about 3 months ago, so behavioral-prediction-mcp is actively maintained.
- Our last check on 2026-10-22 found the source still online.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 92/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-24. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
behavioral-prediction-mcp compared with similar skills
All 4 of these similar skills score higher than behavioral-prediction-mcp; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| behavioral-prediction-mcp (this skill)by ChainAware | 72 | 10 | 3mo ago | MCP Server |
| taste-skillby Leonxlnx | 100 | 110.6k | 1d ago | CLAUDE.md |
| Agent-Reachby Panniantong | 100 | 109.2k | today | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 78.9k | today | CLAUDE.md |
| ai-job-searchby ai-job-search-dev | 100 | 69.8k | today | CLAUDE.md |
Frequently asked questions
- How do I install behavioral-prediction-mcp?
- Run
claude mcp add ChainAware -- npx -y github:ChainAware/behavioral-prediction-mcp. The install tabs above show the steps for each supported agent. - Which AI agents does behavioral-prediction-mcp work with?
- It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
- Is behavioral-prediction-mcp safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 92/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is behavioral-prediction-mcp still maintained?
- The repository was last updated about 3 months ago, so behavioral-prediction-mcp is actively maintained.
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 · x402 payment supported)
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 | ✅* | ChainAware API key — or omit when using x402 payments |
| 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:
• `402 Payment Required` → x402 payment required — compatible clients settle automatically
• `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 | ✅* | ChainAware API key — or omit when using x402 payments |
| 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:
• `402 Payment Required` → x402 payment required — compatible clients settle automatically
• `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 | ✅* | ChainAware API key — or omit when using x402 payments |
| network | string | ✅ | Blockchain network (ETH, BNB,BASE,
Truncated for display — read the full file on GitHub.
Related Skills
taste-skill
110.6kAgent skills that give coding agents taste: art direction, reference images and a browser check instead of generic AI pages
Agent-Reach
109.2kGive your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
headroom
78.9kCompress 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.
ai-job-search
69.8kThe job search that runs on your machine. AI job application framework built on Claude Code: evaluate postings, tailor CVs, write cover letters, prep interviews. Fork it and own it.
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
