ChainAware Behavioral Prediction MCP

Name: ChainaAware Behavioral Prediction
Availability: InStock
Rating: 5 (32 reviews)
Author: ChainAware

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:

Fraud Detection — predict fraudulent wallet behavior before it happens (~98% accuracy on ETH)
Behavioral Analysis — profile wallet intent, risk tolerance, experience, and next likely actions
Rug Pull Detection — forecast whether a smart contract or liquidity pool will rug pull
Token Rank List — rank tokens by holder community strength across chains and categories
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

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-wallet-auditor subagent
For batch screening of many wallets → use chainaware-fraud-detector subagent
For marketing personalization → use chainaware-wallet-marketer subagent

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

Step-by-Step Workflow

For wallet fraud screening

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

For behavioral profiling / personalization

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

For rug pull / contract safety checks

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

For token ranking / discovery

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

For full due diligence (multi-tool)

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

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

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

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."

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."

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?"

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."

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."

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

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
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

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

Go to Settings → Integrations → Add integration
Name: ChainAware Behavioural Prediction MCP Server
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

Open ChatGPT Settings → Apps / Connectors → Add Connector
Name: ChainAware Behavioural Prediction MCP Server
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

Tips for Success

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

Related Subagents (Claude Code)

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

Subagent	Use When
`chainaware-wallet-auditor`	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

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/

Data & Privacy

What data leaves your environment

Every tool call transmits the following to https://prediction.mcp.chainaware.ai/sse:

Field	Example	Notes
`walletAddress`	`0xABC...`	Pseudonymous on-chain identifier — not PII
`network`	`ETH`	Chain identifier only
`apiKey`	(your key)	Sourced from `CHAINAWARE_API_KEY` env var; never logged

What is NOT sent: names, emails, IP addresses, private keys, raw transaction history, or any off-chain personal data.

API key handling

CHAINAWARE_API_KEY is read from the environment and passed as the apiKey parameter in each tool call. It is never included in output, never written to disk, and never logged by this skill. Treat it as a secret and rotate it regularly.

Integration-specific privacy notes

Claude Code / Cursor: key passed via X-API-Key header — does not appear in URLs or logs
Claude Web / ChatGPT: key must be appended to the SSE URL (?apiKey=...) — these platforms do not support custom SSE headers. Be aware the key will appear in your browser's network tab. Use a restricted-scope key for these integrations.

Operator responsibilities

Wallet addresses are pseudonymous identifiers. Whether they constitute personal data in your jurisdiction depends on your regulatory context (e.g. GDPR, MiCA). Operators processing wallets linked to identified users should perform their own data protection assessment.

Privacy policy: https://chainaware.ai/privacy

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; use restricted-scope keys for browser-based integrations
The server uses SSE (Server-Sent Events) for streaming responses
Rate limits apply depending on your subscription tier

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

Access & Pricing

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

ChainaAware Behavioral Prediction

ChainAware Behavioral Prediction MCP

What This Skill Does

When to Use This Skill

When NOT to Use This Skill

Supported Blockchains

Step-by-Step Workflow

For wallet fraud screening

For behavioral profiling / personalization

For rug pull / contract safety checks

For token ranking / discovery

For full due diligence (multi-tool)

Risk Score Thresholds

Available Tools

1. predictive_fraud — Fraud Detection

2. predictive_behaviour — Behavioral Analysis & Personalization

3. predictive_rug_pull — Rug Pull Detection

4. token_rank_list — Token Ranking by Holder Strength

5. token_rank_single — Single Token Rank & Top Holders

Validation Checkpoints

Input Validation

Output Validation

Example Output

Fraud Check — 0xABC... on ETH

Behavioral Profile — 0xDEF... on BASE

Integration Setup

Claude Code (CLI)

Claude Web / Claude Desktop

Cursor (mcp.json)

ChatGPT Connectors

Node.js

Python

Real-World Use Cases

DeFi Platforms

AI Agent Personalization

Fraud & Compliance

NFT & GameFi

Tips for Success

Related Subagents (Claude Code)

Background Reading

Data & Privacy

What data leaves your environment

API key handling

Integration-specific privacy notes

Operator responsibilities

Security Notes

Error Reference

Access & Pricing

Download

Skill Info

1. `predictive_fraud` — Fraud Detection

2. `predictive_behaviour` — Behavioral Analysis & Personalization

3. `predictive_rug_pull` — Rug Pull Detection

4. `token_rank_list` — Token Ranking by Holder Strength

5. `token_rank_single` — Single Token Rank & Top Holders

Cursor (`mcp.json`)