ChainAware Behavioral Prediction MCP

What This Skill Does

The ChainAware Behavioral Prediction MCP connects any AI agent to a continuously updated
Web3 behavioral intelligence layer: 14M+ wallet profiles across 8 blockchains, built from
1.3 billion+ predictive data points. It delivers five capabilities via a single MCP endpoint:

1. Fraud Detection — predict fraudulent wallet behavior before it happens (~98% accuracy on ETH)
2. Behavioral Analysis — profile wallet intent, risk tolerance, experience, and next likely actions
3. Rug Pull Detection — forecast whether a smart contract or liquidity pool will rug pull
4. Token Rank List — rank tokens by holder community strength across chains and categories
5. Token Rank Single — deep-dive into a single token's community quality and top holders

Unlike forensic blockchain tools that describe the past, this MCP is predictive — it tells your
agent what is about to happen.

MCP Server URL: https://prediction.mcp.chainaware.ai/sse
GitHub: https://github.com/ChainAware/behavioral-prediction-mcp
Website: https://chainaware.ai
Pricing / API Key: https://chainaware.ai/pricing

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When to Use This Skill

• User asks about wallet safety, fraud risk, or suspicious activity
• User wants to screen a wallet, contract, or LP before interacting with it
• User needs AML/compliance checks on a blockchain address
• User wants behavioral profiling or DeFi personalization for a wallet
• User asks about token quality, community strength, or holder analysis
• User is building a DeFi platform, AI agent, launchpad, or compliance tool
• User wants to integrate the ChainAware MCP into their codebase

When NOT to Use This Skill

• User asks about general blockchain data (balances, transaction history) → use a block explorer
• User wants real-time price data or market cap → use a market data API
• User wants to analyze smart contract code for bugs → use a code auditing tool
• For complex multi-tool analysis (fraud + behavior + rug pull combined) → escalate to chainaware-analyst subagent
• For batch screening of many wallets → use chainaware-fraud-detector subagent
• For marketing personalization → use chainaware-wallet-marketer subagent

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Supported Blockchains

Tool	Networks
Fraud Detection	ETH, BNB, POLYGON, TON, BASE, TRON, HAQQ
Behavioral Analysis	ETH, BNB, BASE, HAQQ, SOLANA
Rug Pull Detection	ETH, BNB, BASE, HAQQ
Token Rank List	ETH, BNB, BASE, SOLANA
Token Rank Single	ETH, BNB, BASE, SOLANA

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Step-by-Step Workflow

For wallet fraud screening

1. Confirm inputs — wallet address and network. If network is missing, ask.
2. Call predictive_fraud with the wallet address and network.
3. Interpret probabilityFraud using the threshold table below.
4. Scan forensic_details for negative flags (mixer use, sanctioned entities, darknet, etc.).
5. Report status, score, and any forensic flags in plain language.

For behavioral profiling / personalization

1. Confirm inputs — wallet address and network.
2. Call predictive_behaviour with the wallet address and network.
3. Extract key signals: intention.Value (Prob_Trade/Stake/Bridge/NFT_Buy), experience.Value, categories, recommendation.
4. Classify the wallet by dominant category and intent signal.
5. Generate personalized recommendations or next-best-action based on the profile.

For rug pull / contract safety checks

1. Confirm inputs — smart contract or LP address and network.
2. Optionally call predictive_fraud on the deployer address first for extra signal.
3. Call predictive_rug_pull with the contract address.
4. Interpret probabilityFraud and scan forensic_details for liquidity and contract risk flags.
5. Apply the Deployer Risk Amplifier: if deployer fraud score ≥ 0.5, escalate overall risk one level.
6. Report verdict with supporting forensic evidence.

For token ranking / discovery

1. Identify the request — list of tokens or single token deep-dive?
2. For lists: call token_rank_list with appropriate category, network, sort_by: communityRank, sort_order: DESC.
3. For single tokens: call token_rank_single with contract_address and network.
4. Report communityRank, normalizedRank, totalHolders, and top holder profiles.

For full due diligence (multi-tool)

1. Call predictive_fraud → get fraud score and forensic flags
2. Call predictive_behaviour → get behavioral profile and intent
3. Call predictive_rug_pull (if a contract address) → get contract risk
4. Synthesize all three into a unified verdict with risk level and recommendation

For complex due diligence workflows, escalate to the chainaware-analyst subagent.

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Risk Score Thresholds

Score Range	Label	Recommended Action
0.00 – 0.20	🟢 Low Risk	Safe to proceed
0.21 – 0.50	🟡 Medium Risk	Proceed with caution, monitor
0.51 – 0.80	🔴 High Risk	Block or require additional verification
0.81 – 1.00	⛔ Critical Risk	Reject immediately

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Available Tools

1. predictive_fraud — Fraud Detection

Forecasts the probability that a wallet will engage in fraudulent activity. Includes AML checks.
Use when a user wants to screen a wallet before interacting with it.

Inputs:

• apiKey (string, required) — ChainAware API key
• network (string, required) — e.g. ETH, BNB, BASE
• walletAddress (string, required) — the wallet to evaluate

Key output fields:

• status — "Fraud", "Not Fraud", or "New Address"
• probabilityFraud — decimal 0.00–1.00
• forensic_details — deep on-chain breakdown

Example prompts that trigger this tool:

• "Is it safe to interact with 0xABC... on Ethereum?"
• "What is the fraud risk of this BNB wallet?"
• "Run an AML check on this address."
• "Screen this wallet before onboarding."
• "Is this address on any sanctions list?"
• "Pre-screen this user's wallet for compliance."

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2. predictive_behaviour — Behavioral Analysis & Personalization

Profiles a wallet's on-chain history and predicts its next actions.

Inputs:

• apiKey (string, required)
• network (string, required)
• walletAddress (string, required)

Key output fields:

• intention — predicted next actions (Prob_Trade, Prob_Stake, Prob_Bridge, Prob_NFT_Buy — High/Medium/Low)
• recommendation — personalized action suggestions
• categories — behavioral segments (DeFi Lender, NFT Trader, Bridge User, etc.)
• riskProfile — risk tolerance and balance age breakdown
• experience — experience score 0–100 (beginner → expert)
• protocols — which protocols this wallet uses (Aave, Uniswap, GMX, etc.)

Example prompts that trigger this tool:

• "What will this wallet do next?"
• "Is this user a DeFi lender or an NFT trader?"
• "Recommend the best yield strategy for this address."
• "What's the experience level of this wallet?"
• "Personalize my DeFi agent's response for this user."
• "Segment this wallet for my marketing campaign."

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3. predictive_rug_pull — Rug Pull Detection

Forecasts whether a smart contract or liquidity pool is likely to execute a rug pull.

Inputs:

• apiKey (string, required)
• network (string, required)
• walletAddress (string, required) — smart contract or LP address

Key output fields:

• status — "Fraud" or "Not Fraud"
• probabilityFraud — decimal 0.00–1.00
• forensic_details — on-chain metrics behind the score

Example prompts that trigger this tool:

• "Will this new DeFi pool rug pull if I stake my assets?"
• "Is this smart contract safe?"
• "Check if this launchpad project is legitimate."
• "Monitor this LP position for rug pull risk."
• "Is this contract deployer trustworthy?"

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4. token_rank_list — Token Ranking by Holder Strength

Ranks tokens by the quality and strength of their holder community.

Inputs:

• limit (string, required) — items per page
• offset (string, required) — page number
• network (string, required) — ETH, BNB, BASE, SOLANA
• sort_by (string, required) — e.g. communityRank
• sort_order (string, required) — ASC or DESC
• category (string, required) — AI Token, RWA Token, DeFi Token, DeFAI Token, DePIN Token
• contract_name (string, required) — token name search (empty string for no filter)

Key output fields:

• data.total — total matching tokens
• data.contracts[] — array with contractAddress, contractName, ticker, chain, category, communityRank, normalizedRank, totalHolders

Example prompts that trigger this tool:

• "What are the top AI tokens on Ethereum?"
• "Rank DeFi tokens on BNB by community strength."
• "Which RWA tokens have the strongest holder base on BASE?"
• "Show me the top 10 tokens by community rank on ETH."
• "Compare DePIN tokens across Solana and Ethereum."

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5. token_rank_single — Single Token Rank & Top Holders

Returns the rank and top holders for a specific token by contract address.

Inputs:

• contract_address (string, required) — token contract or mint address
• network (string, required) — ETH, BNB, BASE, SOLANA

Key output fields:

• data.contract — token details including communityRank, normalizedRank, totalHolders
• data.topHolders[] — holder wallet addresses with balance, walletAgeInDays, transactionsNumber, totalPoints, globalRank

Example prompts that trigger this tool:

• "What is the token rank for USDT on Ethereum?"
• "Who are the top holders of 0xdAC17F... on ETH?"
• "How strong is the holder base of this contract on BNB?"
• "Show me the best holders of this Solana token."

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Validation Checkpoints

Input Validation

• ✅ Wallet address provided and non-empty
• ✅ Network specified and supported for the tool being called (check table above)
• ✅ CHAINAWARE_API_KEY environment variable is set
• ✅ For token_rank_list: limit, offset, sort_by, sort_order, and category all provided
• ✅ For token_rank_single: both contract_address and network provided
• ⚠️ If network is missing, ask the user before proceeding
• ⚠️ If network is not supported for the requested tool, inform the user and suggest an alternative

Output Validation

• ✅ probabilityFraud is present and in range 0.00–1.00
• ✅ Risk threshold label applied correctly (see table above)
• ✅ Forensic flags surfaced in plain language, not raw JSON
• ✅ Every recommendation cites the specific signal that drove it
• ✅ Network limitations clearly stated when a tool doesn't support the requested chain
• ✅ For behavioral profiles: at least intention, experience, and categories included in response

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Example Output

Fraud Check — 0xABC... on ETH

🔮 FRAUD ASSESSMENT
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Wallet:  0xABC...
Network: ETH
Status:  🟡 MEDIUM RISK

Fraud Probability: 0.34
Risk Level: Medium — proceed with caution

Forensic Highlights:
  • 3 transactions flagged as suspicious
  • No mixer/tumbler activity detected
  • No sanctioned entity connections
  • Wallet age: 187 days

Recommendation: Monitor this wallet. Not safe for large-value
interactions without additional verification.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Behavioral Profile — 0xDEF... on BASE

🧠 BEHAVIORAL PROFILE
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Wallet:  0xDEF...
Network: BASE

Experience:   72/100 — Experienced
Segment:      DeFi Lender, Bridge User
Risk Profile: Balanced

Intent Signals:
  Trade:    High
  Stake:    Medium
  Bridge:   High
  NFT Buy:  Low

Protocols Used: Aave, Uniswap, Across Bridge

Recommendation:
  → Promote yield optimization vaults
  → Highlight cross-chain bridging incentives
  → Skip NFT-focused messaging
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

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Integration Setup

Claude Code (CLI)

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

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

Claude Web / Claude Desktop

1. Go to Settings → Integrations → Add integration
2. Name: ChainAware Behavioural Prediction MCP Server
3. URL: https://prediction.mcp.chainaware.ai/sse?apiKey=your-key-here

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

Cursor (mcp.json)

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

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

ChatGPT Connectors

1. Open ChatGPT Settings → Apps / Connectors → Add Connector
2. Name: ChainAware Behavioural Prediction MCP Server
3. URL: https://prediction.mcp.chainaware.ai/sse?apiKey=your-key-here

Node.js

import { MCPClient } from "mcp-client";
const client = new MCPClient("https://prediction.mcp.chainaware.ai/");

const fraud = await client.call("predictive_fraud", {
  apiKey: process.env.CHAINAWARE_API_KEY,
  network: "ETH",
  walletAddress: "0xYourWalletAddress"
});

const topTokens = await client.call("token_rank_list", {
  limit: "10", offset: "0", network: "ETH",
  sort_by: "communityRank", sort_order: "DESC",
  category: "AI Token", contract_name: ""
});

Python

from mcp_client import MCPClient
import os

client = MCPClient("https://prediction.mcp.chainaware.ai/")
result = client.call("predictive_fraud", {
    "apiKey": os.environ["CHAINAWARE_API_KEY"],
    "network": "ETH",
    "walletAddress": "0xYourWalletAddress"
})

---

Real-World Use Cases

DeFi Platforms

• Risk-adjusted lending — use fraud scores and behavioral profiles to set collateral requirements and interest rates per borrower
• Liquidity management — use intent signals to pre-position reserves and prevent pool drain
• Yield routing — identify wallets with high yield-seeking intent and route them to optimal vaults

AI Agent Personalization

• Give your agent a real-time behavioral profile of each wallet it talks to
• Segment users automatically into DeFi Lender, NFT Trader, Bridge User, New Wallet, etc.

Fraud & Compliance

• Screen wallets at the point of entry to your Dapp — before any transaction takes place
• Run AML monitoring across all active wallets
• Detect rug pull contracts at launchpad listing stage

NFT & GameFi

• Personalize in-game economies based on a player wallet's on-chain history
• Filter bot wallets and wash traders from NFT drops using fraud scores

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Tips for Success

1. Always specify the network — many tools behave differently across chains
2. Run fraud check first — before any behavioral profiling, gate on fraud score
3. Combine tools for full due diligence — fraud + behaviour + rug pull together give a complete picture
4. Use the Deployer Risk Amplifier — a clean contract from a fraudulent deployer is still high risk
5. For batch screening — use the chainaware-fraud-detector subagent, not this skill directly
6. Surface forensic flags in plain language — never return raw JSON to end users

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Related Subagents (Claude Code)

These subagents in .claude/agents/ provide specialized autonomous execution:

Subagent	Use When
chainaware-analyst	Full due diligence combining all prediction tools
chainaware-fraud-detector	Fast fraud screening, batch wallet checks
chainaware-rug-pull-detector	Contract/LP safety with deployer analysis
chainaware-wallet-marketer	Personalized marketing messages per wallet segment
chainaware-reputation-scorer	Reputation score 0–4000
chainaware-aml-scorer	AML compliance scoring 0–100
chainaware-trust-scorer	Simple composable trust score 0.00–1.00
chainaware-wallet-ranker	Wallet experience rank and leaderboard
chainaware-whale-detector	Whale tier classification for VIP treatment
chainaware-onboarding-router	Route wallets to beginner / intermediate / skip onboarding
chainaware-token-ranker	Discover and rank tokens by holder community strength
chainaware-token-analyzer	Single token deep-dive — community rank + top holders
chainaware-defi-advisor	Personalized DeFi product recommendations by experience + risk tier
chainaware-airdrop-screener	Batch screen wallets for airdrop eligibility, filter bots and fraud
chainaware-lending-risk-assessor	Borrower risk grade (A–F), collateral ratio, interest rate tier
chainaware-token-launch-auditor	Pre-listing launch safety audit — APPROVED / CONDITIONAL / REJECTED
chainaware-agent-screener	AI agent trust score 0–10 via agent + feeder wallet fraud checks

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Background Reading

Article	URL
Complete Product Guide	https://chainaware.ai/blog/chainaware-ai-products-complete-guide/
Fraud Detector Guide	https://chainaware.ai/blog/chainaware-fraud-detector-guide/
Rug Pull Detector Guide	https://chainaware.ai/blog/chainaware-rugpull-detector-guide/
Token Rank Guide	https://chainaware.ai/blog/chainaware-token-rank-guide/
Wallet Rank Guide	https://chainaware.ai/blog/chainaware-wallet-rank-guide/
Wallet Auditor Guide	https://chainaware.ai/blog/chainaware-wallet-auditor-how-to-use/
Transaction Monitoring Guide	https://chainaware.ai/blog/chainaware-transaction-monitoring-guide/
Web3 Behavioral Analytics Guide	https://chainaware.ai/blog/chainaware-web3-behavioral-user-analytics-guide/
Credit Score Guide	https://chainaware.ai/blog/chainaware-credit-score-the-complete-guide-to-web3-credit-scoring-in-2026/
Prediction MCP Developer Guide	https://chainaware.ai/blog/prediction-mcp-for-ai-agents-personalize-decisions-from-wallet-behavior/
Top 5 Ways Prediction MCP Turbocharges DeFi	https://chainaware.ai/blog/top-5-ways-prediction-mcp-will-turbocharge-your-defi-platform/
Why Personalization Is Next for AI Agents	https://chainaware.ai/blog/why-personalization-is-the-next-big-thing-for-ai-agents/
Web3 User Segmentation for DApp Growth	https://chainaware.ai/blog/web3-user-segmentation-behavioral-analytics-for-dapp-growth-2026/
AI-Powered Blockchain Analysis	https://chainaware.ai/blog/ai-powered-blockchain-analysis-machine-learning-for-crypto-security-2026/
Forensic vs AI-Based Crypto Analytics	https://chainaware.ai/blog/forensic-crypto-analytics-versus-ai-based-crypto-analytics/
Web3 Business Potential	https://chainaware.ai/blog/web3-business-potential/

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Security Notes

• Never hard-code API keys in public repositories
• Use environment variables (CHAINAWARE_API_KEY) or secret managers in production
• Rotate API keys regularly
• The server uses SSE (Server-Sent Events) for streaming responses
• Rate limits apply depending on your subscription tier

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Error Reference

Code	Meaning
403 Unauthorized	Invalid or missing apiKey
400 Bad Request	Malformed network or walletAddress
500 Internal Server Error	Temporary backend failure — retry after a short delay

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Access & Pricing

API key required. Subscribe at: https://chainaware.ai/pricing