Prediction MCP¶

The ChainAware Behavioral Prediction MCP is an open-source Model Context Protocol server that gives AI agents real-time predictive intelligence over blockchain wallets and smart contracts - without writing API integration code. It is the trust and fraud intelligence layer for agentic AI systems: fraud scoring, AML screening, Know Your Agent (KYA) verification, and rug pull detection, all consumable by any agent in a single tool call.

Any MCP-compatible AI environment - Claude Code, Claude Desktop, Cursor, or a custom multi-agent pipeline - can connect to the MCP server and immediately gain access to six core prediction tools backed by 20M+ wallet profiles across 8 blockchains.

10
MCP Tools

32
Subagents

8
Blockchains

20M+
Wallet Profiles

MCP Endpoint: https://prediction.mcp.chainaware.ai/sse

GitHub: github.com/ChainAware/behavioral-prediction-mcp

The Six Tools¶

Tool	What It Does	Networks
`predictive_fraud`	Fraud probability + AML forensics for a wallet address	ETH, BNB, POLYGON, TON, BASE, TRON, HAQQ
`predictive_fraud_batch`	Batch fraud detection for a list of wallets - schedules a job, returns `job_id` immediately	ETH, BNB, POLYGON, TON, BASE, TRON, HAQQ
`predictive_behaviour`	Wallet segmentation, intent prediction, experience scoring, personalisation recommendations	ETH, BNB, BASE, HAQQ, SOLANA
`predictive_behaviour_batch`	Batch behavioural profiling for a list of wallets - schedules a job, returns `job_id` immediately	ETH, BNB, BASE, HAQQ, SOLANA
`predictive_rug_pull`	Rug pull risk score for smart contracts and liquidity pools	ETH, BNB, BASE, HAQQ
`token_rank_list`	Ranked list of tokens by holder community strength	ETH, BNB, BASE, SOLANA
`token_rank_single`	Community rank + top holders for a specific token contract	ETH, BNB, BASE, SOLANA
`credit_score`	AI-driven crypto trust score (1-9) combining behavioural analysis, fraud scoring, and social graph signals	ETH
`check_job_status`	Check progress of a batch job - returns completed/failed/pending counts	-
`get_job_results`	Retrieve results of a completed or partial batch job	-

Quick Setup¶

Step 1 - Get an API key

Request access at chainaware.ai/support. Your key is also visible in your account at chainaware.ai/profile.

Step 2 - Register the MCP server

claude mcp add --transport sse chainaware-behavioral-prediction \
  https://prediction.mcp.chainaware.ai/sse \
  --header "X-API-Key: YOUR_API_KEY"

Step 3 - Start using the tools

The six prediction tools are now available in any Claude Code session. Optionally install the 32 pre-built subagents:

git clone https://github.com/ChainAware/behavioral-prediction-mcp.git
cp behavioral-prediction-mcp/.claude/agents/*.md .claude/agents/

Full setup instructions for Claude Desktop, Cursor, and other MCP clients: Setup Guide.

What You Can Build¶

The tools are composable - use them individually or combine them into more powerful workflows.

Fraud gate at wallet-connect
Call predictive_fraud at the moment a wallet connects to your dapp. Block wallets above your risk threshold before they interact with your protocol - in under 100ms.

Personalised onboarding
Call predictive_behaviour after connection to get experience level, behavioral categories, and personalised recommendations. Route DeFi veterans directly to advanced features; show newcomers a guided flow.

Rug pull screening
Call predictive_rug_pull before listing a token or pool on your DEX. Block high-risk contracts from appearing in your UI automatically.

Token discovery and due diligence
Call token_rank_list to find the strongest tokens in a category by holder community quality. Follow up with token_rank_single to deep-dive into a specific token's top holders.

Credit scoring for lending
Call credit_score to get a 1-9 trust rating for a borrower wallet. Combine with predictive_fraud for a complete lending risk picture before originating undercollateralised loans.

Full wallet intelligence pipeline
Chain the wallet tools together: predictive_fraud for risk, credit_score for lending trust, predictive_behaviour for profile - then apply the result to onboarding, credit, or personalisation decisions.

The 32 Subagents¶

The GitHub repository includes 32 pre-built Claude Code subagents that wrap these tools with focused prompts and decision logic - ready-made agents for fraud detection, AML scoring, compliance screening, onboarding routing, whale detection, credit assessment, GameFi screening, DAO governance, and more.

See all 32 subagents →

Authentication¶

Every tool call requires an apiKey parameter. Set it as an environment variable rather than hardcoding it:

export CHAINAWARE_API_KEY="your-api-key-here"

Reference it in code as process.env.CHAINAWARE_API_KEY (Node.js) or os.environ["CHAINAWARE_API_KEY"] (Python). Never commit the key to version control.

Tool Reference¶

predictive_fraud - fraud probability + AML forensics
predictive_fraud_batch - batch fraud detection; returns job_id immediately
predictive_behaviour - behavioural profiling + intent prediction
predictive_behaviour_batch - batch behavioural profiling; returns job_id immediately
predictive_rug_pull - smart contract rug pull risk
token_rank_list - ranked token list by holder community strength
token_rank_single - single token deep-dive + top holders
credit_score - crypto trust score (1-9) for lending and credit decisions
check_job_status - check batch job progress
get_job_results - retrieve completed batch job results