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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 fourteen prediction tools backed by 20M+ wallet profiles across 8 blockchains.

14
MCP Tools
34
Subagents
8
Blockchains
20M+
Wallet Profiles

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

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

MCP 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
run_token_audit Deep smart contract audit - ownership, liquidity, supply, transfer, approve, permit, reentrancy, and honeypot analysis eth, bsc, base, arbitrum, avalanche, optimism, polygon
get_token_audit_result Retrieve token audit results - poll this when run_token_audit returns status: "queued" eth, bsc, base, arbitrum, avalanche, optimism, polygon
agents_trust_score_list Paginated list of ERC-8004 AI agents with 0-1000 trust scores ETH, BNB, Base, AVAX, Mantle
agents_trust_score_single Full trust profile for a single ERC-8004 registered AI agent ETH, BNB, Base, AVAX, Mantle
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 prediction tools are now available in any Claude Code session. Optionally install the 34 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 34 Subagents

The GitHub repository includes 34 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, token contract auditing, and ERC-8004 agent trust screening.

See all 34 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

Further Reading

See also: For AI Agents | Enterprise API | GitHub Repository