Open App

Prediction MCP

Key Takeaways
- Open-source MCP server with 14 prediction tools - free to test, no signup required
- Connects to Claude Code, Claude Desktop, Cursor, and any MCP-compatible agent in one command
- Covers fraud detection, behavioral profiling, rug pull screening, credit scoring, token auditing, and ERC-8004 agent trust across 8 blockchains and 23M+ wallet profiles
- 34 pre-built subagents available for zero-code integration

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 23M+ wallet profiles across 8 blockchains.

As AI agents become autonomous economic actors - paying for services, subscribing, transacting 24/7 - the fraud prevention and identity systems built for humans don't carry over. The agentic economy needs its own native trust infrastructure. ChainAware's MCP server is that layer: a single tool call away from a real-time answer on whether a wallet, token, or pool is safe to interact with.

14
MCP Tools
34
Subagents
8
Blockchains
23M+
Wallet Profiles

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

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

How It Fits Your Agent's Decision Loop

01 - Agent proposes action
Your agent decides to swap, lend, pay, or counter-party with an on-chain address.

02 - MCP tool call
Agent calls get_trust_score / audit_token / check_rugpull_risk over SSE - no custom integration required.

03 - Structured risk object returned
Score, flags, and evidence return in milliseconds, sourced from live block scans.

04 - Agent acts or aborts
Execute, downsize, or decline the transaction autonomously - with the reasoning logged.

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

Agent Trust Score index (live): 377,608 agents indexed - average score 420, 106,254 flagged high-risk (0-200 tier). Behavior-based scoring derived from on-chain history, counterparty risk, and fraud probability - not peer voting, which can be Sybil-farmed with throwaway wallets.
| 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

Free to test - no signup required. Point any MCP client at the SSE endpoint and start calling tools immediately. For production use, get an API key at chainaware.ai/support (also visible at chainaware.ai/profile).

Step 1 - Connect (Claude Code)

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

Step 1 - Connect (Claude Desktop, LangChain, custom stack)

{
  "mcpServers": {
    "chainaware": {
      "url": "https://prediction.mcp.chainaware.ai/sse"
    }
  }
}

Step 2 - Start using the tools

The prediction tools are now available in any MCP-compatible 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/

Examples: github.com/ChainAware/examples - ready-to-run agent examples for fraud gating, token screening, and onboarding workflows.

Full setup instructions for all 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

Frequently Asked Questions

What is the ChainAware Behavioral 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 via 14 tools - without writing custom API integration code.

Is the Prediction MCP free to use?
Yes. The MCP endpoint is free to test with no signup required. Point any MCP client at https://prediction.mcp.chainaware.ai/sse and call tools immediately. An API key is required for production use and is available at chainaware.ai/support.

Which blockchains does the Prediction MCP support?
Support varies by tool. Fraud detection covers ETH, BNB, POLYGON, TON, BASE, TRON, and HAQQ. Behavioral profiling covers ETH, BNB, BASE, HAQQ, and SOLANA. Rug pull detection covers ETH, BNB, BASE, and HAQQ. Token ranking covers ETH, BNB, BASE, and SOLANA.

What is the difference between predictive_fraud and predictive_behaviour?
predictive_fraud scores a wallet's fraud risk (0-1) and provides AML forensics - it answers "is this wallet a threat?". predictive_behaviour profiles a wallet's on-chain behavior - segments, experience level, DeFi activity patterns - and answers "what kind of user is this?". Both tools can be combined for a complete wallet intelligence picture.

How do I connect the Prediction MCP to Claude Code?
Run: claude mcp add --transport sse chainaware-behavioral-prediction https://prediction.mcp.chainaware.ai/sse. For production use, add --header "X-API-Key: YOUR_API_KEY". The 14 prediction tools are then available in any Claude Code session.

Can I use the Prediction MCP with agents other than Claude?
Yes. Any MCP-compatible environment works - Claude Desktop, Cursor, LangChain, and custom multi-agent pipelines. Add the server URL to your mcpServers config and the tools are immediately available.

Further Reading

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

Last updated: