Behavioral Prediction▌
by chainaware
Behavioral Prediction: AI tools for wallet behavior analysis, fraud detection and rug-pull prediction to secure crypto a
Provides AI-powered tools to analyze wallet behavior prediction, fraud detection and rug pull prediction.
github stars
★ 1
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / DeFi platforms protecting users from scams
- / Developers building wallet security features
- / Risk assessment for blockchain interactions
capabilities
- / Predict fraudulent wallet activity with 98% accuracy
- / Perform AML and anti-money laundering checks
- / Detect rug pull risks in DeFi projects
- / Score wallet and contract trustworthiness
- / Analyze blockchain addresses for security risks
what it does
Provides AI-powered blockchain security analysis to predict fraudulent wallet behavior, detect scams, and assess rug pull risks before they happen. Requires API key access.
about
Behavioral Prediction is a community-built MCP server published by chainaware that provides AI assistants with tools and capabilities via the Model Context Protocol. Behavioral Prediction: AI tools for wallet behavior analysis, fraud detection and rug-pull prediction to secure crypto a It is categorized under auth security, finance.
how to install
You can install Behavioral Prediction in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server supports remote connections over HTTP, so no local installation is required.
license
MIT
Behavioral Prediction is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
🧠 ChainAware Behavioural Prediction MCP Server
MCP Server Name: ChainAware Behavioural Prediction MCP
Category: Web3 / Security / DeFi Analytics
Status: Public tools – Private backend
Access: By request (API key)
Server URL: [https://prediction.mcp.chainaware.ai/sse]
Repository: [https://github.com/ChainAware/behavioral-prediction-mcp]
Website: [https://chainaware.ai/]
Twitter: [https://x.com/ChainAware/]
<!-- MCP -->mcp-name: io.github.ChainAware/chainaware-behavioral-prediction-mcp
📖 Description
The Behavioural Prediction MCP Server provides AI-powered tools to analyze wallet behaviour prediction,fraud detection and rug pull prediction.
Developers and platforms can integrate these tools through the MCP protocol to safeguard DeFi users, monitor liquidity risks, and score wallet or contract trustworthiness.
All tools follow the Model Context Protocol (MCP) and can be consumed via MCP-compatible clients.
⚙️ Available Tools
1. Predictive Fraud Detection Tool
ID: predictive_fraud
Description: This AI‑powered algorithm forecasts the likelihood of fraudulent activity on a given wallet address before it happens (≈98% accuracy), and performs AML/Anti‑Money‑Laundering checks. Use this when your user wants a risk assessment or early‑warning on a blockchain address.
➡️ Example Use Cases:
• Is it safe to intercant with vitalik.eth ?
• What is the fraudulent status of this address ?
• Is my new wallet at risk of being used for fraud?
Inputs:
| Name | Type | Required | Description |
|---|---|---|---|
apiKey | string | ✅ | API key for authentication |
network | string | ✅ | Blockchain network (ETH, BNB,POLYGON,TON,BASE, TRON, HAQQ) |
walletAddress | string | ✅ | The wallet address to evaluate |
Outputs (JSON):
{
"message": "string", // Human‑readable status message
"walletAddress": "string", // hex address
"status": "Fraud", // Fraudelent status (Fraud,Not Fraud,New Address)
"probabilityFraud": "0.00–1.00", // Decimal probability
"token": "string", //
"lastChecked": "ISO‑8601 timestamp",
"forensic_details": { // Deep forensic breakdown
/* ...other metrics... */
},
"createdAt": "ISO‑8601 timestamp",
"updatedAt": "ISO‑8601 timestamp"
}
Error cases:
• `403 Unauthorized` → invalid `apiKey`
• `400 Bad Request` → malformed `network` or `walletAddress`
• `500 Internal Server Error` → temporary downstream failure
2. Predictive Behaviour Analysis Tool
ID: predictive_behaviour
Description: This AI‑driven engine projects what a wallet address intentions or what address is likely to do next, profiles its past on‑chain history, and recommends personalized actions.
Use this when you need:
• Next‑best‑action predictions and intentions(“Will this address deposit, trade, or stake?”)
• A risk‑tolerance and experience profile
• Category segmentation (e.g. NFT, DeFi, Bridge usage)
• Custom recommendations based on historical patterns
➡️ Example Use Cases:
• “What will this address do next?”
• “Is the user high‑risk or experienced?”
• “Recommend the best DeFi strategies for 0x1234... on ETH network.”
Inputs:
| Name | Type | Required | Description |
|---|---|---|---|
apiKey | string | ✅ | API key for authentication |
network | string | ✅ | Blockchain network (ETH, BNB,BASE,HAQQ,SOLANA) |
walletAddress | string | ✅ | The wallet address to evaluate |
Outputs (JSON):
{
"message": "string", // e.g. “Success” or error text
"walletAddress": "string", // echoed input
"status": "string", // Fraudelent status (Fraud,Not Fraud,New Address)
"probabilityFraud": "0.00–1.00", // decimal fraud score
"lastChecked": "ISO‑8601 timestamp", // e.g. “2025‑01‑03T16:19:13.000Z”
"forensic_details": { /* dict of forensic metrics */ },
"categories": [ { "Category":"string", "Count":int }, … ],
"riskProfile": [ { "Category":"string", "Balance_age":float }, … ],
"segmentInfo": "JSON‑string of segment counts",
"experience": { "Type":"Experience", "Value":int },
"intention": {
"Type":"Intentions",
"Value": { "Prob_Trade":"High", "Prob_Stake":"Medium", … }
},
"protocols": [ { "Protocol":"string","Count":int }, … ],
"recommendation": { "Type":"Recommendation", "Value":[ "string", … ] },
"createdAt": "ISO‑8601 timestamp",
"updatedAt": "ISO‑8601 timestamp"
}
Error cases:
• `403 Unauthorized` → invalid `apiKey`
• `400 Bad Request` → malformed `network` or `walletAddress`
• `500 Internal Server Error` → temporary downstream failure
3. Predictive Rug‑Pull Detection Tool
ID: predictive_rug_pull
Description: This AI‑powered engine forecasts which liquidity pools or contracts are likely to perform a “rug pull” in the future. Use this when you need to warn users before they deposit into risky pools or to monitor smart‑contract security on-chain.
➡️ Example Use Cases:
• “Will this new DeFi pool rug‑pull if I stake my assets?”
• “Monitor my LP position for potential future exploits.”
Inputs:
| Name | Type | Required | Description |
|---|---|---|---|
apiKey | string | ✅ | API key for authentication |
network | string | ✅ | Blockchain network (ETH, BNB, BASE, HAQQ) |
walletAddress | string | ✅ | Smart contract or liquidity pool address |
Outputs (JSON):
{
"message": "Success",
"contractAddress": "0x1234...",
"status": "Fraud",
"probabilityFraud": 0.87,
"lastChecked": "2025-10-25T12:45:00Z",
"forensic_details": { /* dict of on‑chain metrics */ },
"createdAt": "2025-10-25T12:45:00Z",
"updatedAt": "2025-10-25T12:45:00Z"
}
Error cases:
• `403 Unauthorized` → invalid `apiKey`
• `400 Bad Request` → malformed `network` or `walletAddress`
• `500 Internal Server Error` → temporary downstream failure
4. Token Rank List Tool
ID: token_rank_list
Description: TokenRank analyzes the community of token holders and ranks every token by the strength of its holders. The stronger the token holders, the stronger the token! Use this when you need to know token rank of a token or tokens or compare between different categories and chains. You can use search,filter and sort and pagination which returns a list of tokens.
➡️ Example Use Cases:
– “Which is the best token on AI Token category?”
– “Compare x token in ETH chain and BNB chain?”
Inputs:
| Name | Type | Required | Description |
|---|---|---|---|
limit | string | ✅ | Number of items ot fetch during pagination |
offset | string | ✅ | Page number(offset) during pagination |
network | string | Blockchain network to filter (ETH, BNB, BASE, SOLANA) | |
sort_by | string | Sort the returnet tokens based on (e.g.: 'communityRank') | |
sort_order | string | 'ASC' or 'DESC' sorting the value of sort_by | |
category | string | Filter based on category of the token (e.g. 'AI Token','RWA Token','DeFi Token','DeFAI Token','DePIN Token') | |
contract_name | string | Search based on contract name |
Outputs (JSON):
{
"message": "string", // e.g. “Successfully fetched records” or error description
"data": {
"total": 0, // integer — total number of matching contracts
"contracts": [
{
"contractAddress": "string", // unique contract or mint address (chain-specific format)
"contractName": "string", // human-readable token name
"ticker": "string", // token symbol (usually uppercase, but not guaranteed)
"chain": "string", // blockchain network (e.g. SOLANA | ETH | BNB | BASE)
"category": "string", // primary category label
---
FAQ
- What is the Behavioral Prediction MCP server?
- Behavioral Prediction is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Behavioral Prediction?
- This profile displays 48 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.7★★★★★48 reviews- ★★★★★Mateo Srinivasan· Dec 24, 2024
I recommend Behavioral Prediction for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Carlos Ghosh· Dec 16, 2024
Behavioral Prediction is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Dhruvi Jain· Dec 12, 2024
Useful MCP listing: Behavioral Prediction is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Ren Yang· Dec 12, 2024
We evaluated Behavioral Prediction against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Sakura Perez· Nov 15, 2024
Behavioral Prediction is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Ama Reddy· Nov 7, 2024
We evaluated Behavioral Prediction against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Oshnikdeep· Nov 3, 2024
Behavioral Prediction reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Maya Brown· Nov 3, 2024
Behavioral Prediction is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Kwame Anderson· Oct 26, 2024
Behavioral Prediction has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Ganesh Mohane· Oct 22, 2024
I recommend Behavioral Prediction for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
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