Llama Maverick Hub MCP Server
Author: Yobie Benjamin
Version: 0.2
Date: July 28, 2025
Overview
The Llama Maverick Hub MCP Server is a revolutionary orchestration platform that positions Llama Maverick as the central AI brain connecting to and coordinating multiple MCP-enabled services. Unlike traditional MCP servers that provide tools to AI models, this hub makes Llama the orchestrator that intelligently manages and routes requests across multiple MCP services like Stripe, GitHub, databases, and more.
Key Innovation
This is a Meta-MCP Server that acts as both:
- MCP Server: Exposes unified tools to Claude Desktop or other MCP clients
- MCP Client: Connects to multiple external MCP services (Stripe, GitHub, etc.)
- AI Orchestrator: Uses Llama Maverick to make intelligent routing and workflow decisions
Architecture
┌─────────────────┐
│ Claude Desktop │
│ (MCP Client) │
└────────┬────────┘
│ MCP Protocol
▼
┌─────────────────────────────────────┐
│ Llama Maverick Hub MCP Server │
│ ┌─────────────────────────────┐ │
│ │ Llama Maverick AI Brain │ │
│ │ (Orchestration & Routing) │ │
│ └─────────────────────────────┘ │
│ │ │ │ │
│ ┌──────▼───┬────▼───┬────▼───┐ │
│ │ Service │ Service│ Service│ │
│ │ Registry │ Router │ Manager│ │
│ └──────────┴────────┴────────┘ │
└─────────┬──────────┬──────────┬────┘
│ │ │
MCP Protocol MCP MCP Protocol
│ Protocol │
▼ ▼ ▼
┌──────────┐ ┌──────┐ ┌──────────┐
│ Stripe │ │GitHub│ │ Database │
│ MCP │ │ MCP │ │ MCP │
└──────────┘ └──────┘ └──────────┘
Features
🧠 AI-Driven Orchestration
- Intelligent Routing: Llama analyzes requests and routes to the best service
- Workflow Planning: AI designs multi-step workflows across services
- Error Recovery: Smart error analysis and retry strategies
- Result Synthesis: Combines results from multiple services intelligently
🔗 Multi-Service Integration
- Service Discovery: Automatic discovery of MCP service capabilities
- Unified Tool Access: Single interface to tools from all connected services
- Parallel Execution: Query multiple services simultaneously
- Service Health Monitoring: Track availability and performance
🔄 Advanced Workflows
- Multi-Step Operations: Chain operations across different services
- Dependency Management: Handle complex step dependencies
- Context Passing: Share data between workflow steps
- Retry Policies: Configurable retry strategies per step
🎯 Real-World Integrations
- Stripe MCP: Complete payment workflows with orchestration
- GitHub MCP: Repository management with AI assistance
- Database MCP: Data operations with intelligent queries
- Custom Services: Easy integration of any MCP service
Installation
Prerequisites
- Node.js 18+ and npm
- Ollama with Llama model installed
- Claude Desktop (for MCP client)
- MCP Services you want to connect (e.g., Stripe MCP)
Quick Start
# Clone the repository
git clone https://github.com/yobieben/llama-maverick-hub-mcp.git
cd llama-maverick-hub-mcp
# Install dependencies
npm install
# Build the project
npm run build
# Configure services (edit config.json)
cp config.example.json config.json
# Start the hub server
npm start
Installing Ollama and Llama
# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh
# Pull Llama model
ollama pull llama3.2
# Verify installation
ollama list
Configuration
Basic Configuration (config.json)
{
"hub": {
"name": "llama-maverick-hub",
"version": "0.2.0",
"port": 8080,
"logLevel": "info"
},
"llama": {
"model": "llama3.2",
"baseUrl": "http://localhost:11434",
"contextWindow": 8192,
"defaultTemperature": 0.7
},
"services": [
{
"id": "stripe",
"name": "Stripe MCP",
"description": "Stripe payment processing",
"transport": "stdio",
"endpoint": "stripe-mcp",
"enabled": true,
"command": "npx",
"args": ["-y", "@stripe/mcp-server"],
"reconnectPolicy": {
"maxRetries": 5,
"retryDelayMs": 5000,
"backoffMultiplier": 2
}
},
{
"id": "github",
"name": "GitHub MCP",
"description": "GitHub repository management",
"transport": "stdio",
"endpoint": "github-mcp",
"enabled": true,
"command": "github-mcp-server",
"args": ["--token", "${GITHUB_TOKEN}"]
}
],
"orchestration": {
"maxConcurrentOperations": 10,
"defaultTimeout": 30000,
"retryPolicy": {
"maxRetries": 3,
"retryDelayMs": 1000
}
}
}
Claude Desktop Configuration
Add to your Claude Desktop config:
{
"mcpServers": {
"llama-hub": {
"command": "node",
"args": ["/path/to/llama-maverick-hub-mcp/dist/index.js"],
"env": {
"LLAMA_HUB_LOG_LEVEL": "info",
"STRIPE_API_KEY": "your_stripe_key",
"GITHUB_TOKEN": "your_github_token"
}
}
}
}
Environment Variables
# Hub Configuration
LLAMA_HUB_NAME=llama-maverick-hub
LLAMA_HUB_PORT=8080
LLAMA_HUB_LOG_LEVEL=debug
# Llama Configuration
LLAMA_HUB_LLAMA_MODEL=llama3.2
LLAMA_HUB_LLAMA_BASE_URL=http://localhost:11434
# Service Configuration
LLAMA_HUB_SERVICE_STRIPE_ENABLED=true
LLAMA_HUB_SERVICE_STRIPE_COMMAND=npx
LLAMA_HUB_SERVICE_GITHUB_ENABLED=true
# Security
LLAMA_HUB_ENABLE_AUTH=false
LLAMA_HUB_API_KEYS=key1,key2
Usage Examples
Basic Tool Execution
When you interact with Claude Desktop, you can now access tools from all connected services:
User: "Create a new Stripe customer and set up a subscription"
Claude uses: stripe_create_customer, stripe_create_subscription
Hub routes to: Stripe MCP service
Llama assists: Validates data, handles errors
Intelligent Routing
User: "Process a payment for this customer"
Llama analyzes:
- Customer location
- Payment amount
- Risk factors
Llama decides:
- Route to Stripe for low-risk
- Route to fraud service for high-risk
- Use alternative processor for specific regions
Multi-Service Workflows
User: "Onboard a new customer with payment"
Hub executes workflow:
1. Create Stripe customer
2. Set up payment method
3. Create subscription
4. Store in database
5. Send welcome email
6. Update analytics
All orchestrated by Llama Maverick!
Parallel Service Queries
User: "Get customer information from all systems"
Hub queries simultaneously:
- Stripe for payment data
- Database for profile
- Analytics for behavior
- Support for tickets
Llama synthesizes comprehensive report
Advanced Features
Custom Workflows
Create complex multi-service workflows:
orchestrator.registerWorkflow({
id: 'customer-lifecycle',
name: 'Complete Customer Lifecycle',
description: 'Onboard, activate, and monitor customer',
steps: [
{
id: 'stripe-setup',
service: 'stripe',
tool: 'create_customer',
arguments: { /* ... */ }
},
{
id: 'database-store',
service: 'database',
tool: 'insert_record',
arguments: { /* ... */ },
dependsOn: ['stripe-setup']
},
{
id: 'email-welcome',
service: 'email',
tool: 'send_email',
arguments: { /* ... */ },
dependsOn: ['database-store']
}
]
});
AI-Powered Decision Making
Llama Maverick makes intelligent decisions:
// Llama analyzes request and determines best approach
const routing = await llamaService.analyzeRouting(
userRequest,
availableServices
);
// Llama plans multi-step workflow
const workflow = await llamaService.planWorkflow(
goal,
services,
tools
);
// Llama synthesizes results from multiple sources
const summary = await llamaService.synthesizeResults(
serviceResults,
originalRequest
);
Service Health Monitoring
Automatic failover and recovery:
// Registry tracks service health
registry.on('service:unhealthy', (service) => {
// Llama determines fallback strategy
const fallback = llamaService.determineFallback(service);
// Route to alternative service
orchestrator.reroute(service, fallback);
});
Real-World Use Cases
1. E-Commerce Platform
Workflow: Complete Order Processing
- Validate inventory (Database MCP)
- Process payment (Stripe MCP)
- Update inventory (Database MCP)
- Create shipping label (Shipping MCP)
- Send confirmation (Email MCP)
- Update analytics (Analytics MCP)
2. SaaS Subscription Management
Workflow: Subscription Lifecycle
- Create customer (Stripe MCP)
- Provision resources (Cloud MCP)
- Set up monitoring (Monitoring MCP)
- Configure billing (Stripe MCP)
- Send onboarding (Email MCP)
3. Financial Services
Workflow: Loan Application
- Credit check (Credit MCP)
- Risk assessment (Risk MCP)
- Document verification (Document MCP)
- Approval workflow (Workflow MCP)
- Fund disbursement (Banking MCP)
4. Developer Tools
Workflow: CI/CD Pipeline
- Code analysis (GitHub MCP)
- Run tests (Testing MCP)
- Build artifacts (Build MCP)
- Deploy to cloud (Cloud MCP)
- Update monitoring (Monitoring MCP)
API Reference
Hub Tools
hub_execute_workflow
Execute a predefined multi-service workflow.
{
"name": "hub_execute_workflow",
"arguments": {
"workflowId": "customer-onboarding",
"parameters": {
"email": "customer@example.com",
"plan": "premium"
}
}
}
hub_smart_route
Use Llama AI to intelligently route requests.
{
"name": "hub_smart_route",
"arguments": {
"query": "Process payment for high-risk customer",
"context": {
"amount": 1000,
"currency": "USD",
"risk_score": 85
}
}
}
hub_parallel_query
Query multiple services in parallel.
{
"name": "hub_parallel_query",
"arguments": {
"services": ["stripe", "database", "analytics"],
"query": "Get customer profile"
}
}
Service Tools
All tools from connected services are available with service prefix:
stripe_create_customerstripe_create_chargegithub_create_repogithub_create_issuedatabase_querydatabase_insert
Development
Project Structure
llama-maverick-hub-mcp/
├── src/
│ ├── index.ts # Main server entry point
│ ├── orchestrator/
│ │ └── hub-orchestrator.ts # Core orchestration logic
│ ├── registry/
│ │ └── service-registry.ts # Service management
│ ├── clients/
│ │ └── mcp-client-manager.ts # MCP client connections
│ ├── services/
│ │ └── llama-service.ts # Llama AI integration
│ ├── config/
│ │ └── config-manager.ts # Configuration management
│ └── integrations/
│ └── stripe-integration.ts # Stripe-specific workflows
├── config.json # Hub configuration
├── package.json # Node.js dependencies
├── tsconfig.json # TypeScript configuration
└── README.md # This file
Adding New Services
- Define Service Configuration:
{
"id": "myservice",
"name": "My Service MCP",
"transport": "stdio",
"command": "my-service-mcp",
"enabled": true
}
- Create Integration Module (optional):
export class MyServiceIntegration {
registerWorkflows() {
// Define service-specific workflows
}
}
- Register with Hub:
const integration = new MyServiceIntegration(orchestrator);
integration.registerWorkflows();
Testing
# Run tests
npm test
# Run specific test
npm test -- --grep "orchestrator"
# Test with coverage
npm run test:coverage
Debugging
Enable debug logging:
export LLAMA_HUB_LOG_LEVEL=debug
npm start
View service connections:
# Check service status
curl http://localhost:8080/status
# View service registry
curl http://localhost:8080/services
Monitoring
Metrics
The hub exposes metrics for monitoring:
- Service availability
- Request latency
- Workflow execution time
- Error rates
- Llama inference time
Health Checks
# Hub health
curl http://localhost:8080/health
# Service health
curl http://localhost:8080/health/stripe
Logging
Structured logging with Winston:
logger.info('Workflow executed', {
workflowId: 'customer-onboarding',
duration: 1234,
steps: 6,
success: true
});
Troubleshooting
Common Issues
Llama Connection Failed
Error: Failed to connect to Ollama
Solution: Ensure Ollama is running: ollama serve
Service Not Connecting
Error: Failed to connect to service: stripe
Solution: Check service command and arguments in config
Workflow Timeout
Error: Workflow execution timeout
Solution: Increase timeout in orchestration config
Debug Mode
Enable verbose logging:
// In code
logger.level = 'debug';
// Via environment
LLAMA_HUB_LOG_LEVEL=debug npm start
Security
Best Practices
- API Key Management: Use environment variables for sensitive keys
- Service Isolation: Run services in separate processes
- Rate Limiting: Configure per-service rate limits
- Audit Logging: Log all workflow executions
- Error Handling: Never expose internal errors to clients
Authentication
Enable authentication in config:
{
"security": {
"enableAuth": true,
"apiKeys": ["key1", "key2"]
}
}
Performance
Optimization Tips
- Cache Service Discoveries: Reduce repeated capability queries
- Parallel Execution: Use
hub_parallel_queryfor multi-service operations - Connection Pooling: Reuse MCP client connections
- Workflow Optimization: Minimize step dependencies
- Llama Tuning: Adjust temperature for faster inference
Benchmarks
Typical performance metrics:
- Service connection: < 100ms
- Tool execution: < 500ms
- Workflow step: < 1s
- Llama inference: < 2s
- Full workflow: < 10s
Contributing
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
Areas for Contribution
- New service integrations
- Workflow templates
- Performance optimizations
- Documentation improvements
- Test coverage
License
MIT License - see LICENSE file
Support
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Email: yobie.benjamin@example.com
Acknowledgments
- Anthropic for the MCP protocol
- Meta for Llama models
- Ollama for local model hosting
- Stripe for payment MCP example
- The open-source community
Built with ❤️ by Yobie Benjamin
Orchestrating the future of AI-driven service integration