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Llama Maverick Hub MCP Server

by YobieBen
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TypeScript
Hybrid
MIT License

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

Related MCP server: Multi-Model Advisor

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

  1. Node.js 18+ and npm

  2. Ollama with Llama model installed

  3. Claude Desktop (for MCP client)

  4. 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

  1. Define Service Configuration:

{ "id": "myservice", "name": "My Service MCP", "transport": "stdio", "command": "my-service-mcp", "enabled": true }

  1. Create Integration Module (optional):

export class MyServiceIntegration { registerWorkflows() { // Define service-specific workflows } }

  1. 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

  1. API Key Management: Use environment variables for sensitive keys

  2. Service Isolation: Run services in separate processes

  3. Rate Limiting: Configure per-service rate limits

  4. Audit Logging: Log all workflow executions

  5. Error Handling: Never expose internal errors to clients

Authentication

Enable authentication in config:

{ "security": { "enableAuth": true, "apiKeys": ["key1", "key2"] } }

Performance

Optimization Tips

  1. Cache Service Discoveries: Reduce repeated capability queries

  2. Parallel Execution: Use hub_parallel_query for multi-service operations

  3. Connection Pooling: Reuse MCP client connections

  4. Workflow Optimization: Minimize step dependencies

  5. 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

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

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