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_customer
stripe_create_charge
github_create_repo
github_create_issue
database_query
database_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_query for 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

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A meta-MCP server that uses Llama AI as an orchestrator to intelligently route requests and coordinate workflows across multiple MCP services like Stripe, GitHub, and databases. Enables complex multi-service operations with AI-driven decision making and parallel execution capabilities.

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