YobieBen/llama4-maverick-mcp

🦙 Llama 4 Maverick MCP Server

Author: Yobie Benjamin
Version: 0.9
Date: August 1, 2025

A comprehensive Model Context Protocol (MCP) server that enables seamless integration of Llama models with Claude Desktop and other MCP-compatible clients. This server provides a bridge between local Llama models and AI assistants, enabling powerful tool use, function calling, and enhanced capabilities.

📚 Table of Contents

• What Would You Use This Llama MCP Server For?
• Overview
• Key Features
• System Requirements
• Complete Installation Guide
• Configuration Deep Dive
• Available Tools
• Usage & Experimentation Guide
• Real-World Examples
• Performance Optimization
• Troubleshooting
• Security Best Practices
• Contributing

🎯 What Would You Use This Llama MCP Server For?

The Power of Local AI + Claude Desktop

This MCP server unlocks a revolutionary way to work with AI by combining the best of both worlds: Claude's advanced reasoning capabilities with your own locally-hosted Llama models. Here's what this enables:

1. Privacy-First AI Workflows 🔒

• Sensitive Document Processing: Process confidential documents, medical records, legal contracts, or proprietary code without sending data to the cloud
• Corporate Compliance: Meet strict data residency requirements while still leveraging AI
• Personal Data Analysis: Analyze personal finances, health data, or private communications locally
• Intellectual Property Protection: Work with trade secrets, patents, or unreleased products

Example Use Case: A law firm can use Claude Desktop to analyze confidential client documents using a local Llama model, ensuring complete data privacy while maintaining powerful AI capabilities.

2. Specialized Model Experimentation 🧪

• Fine-Tuned Models: Deploy your own fine-tuned Llama models for specific domains
• Custom Training: Use models trained on your organization's data
• Model Comparison: Switch between different Llama variants (7B, 13B, 70B) based on task requirements
• Parameter Tuning: Experiment with temperature, top-p, and other generation parameters in real-time

Example Use Case: A medical research team can use their fine-tuned medical Llama model through Claude Desktop for analyzing research papers and generating hypotheses.

3. Hybrid AI Architectures 🔄

• Best of Both Worlds: Use Claude for reasoning and planning, Llama for generation
• Load Balancing: Offload certain tasks to local models to reduce API costs
• Fallback Systems: Use local models when cloud services are unavailable
• Complementary Strengths: Leverage each model's unique capabilities

Example Use Case: Use Claude to plan a complex software architecture, then use a code-specialized Llama model to generate the implementation details.

4. Development and Testing Environment 💻

• Rapid Prototyping: Test AI features without API rate limits or costs
• Offline Development: Work on AI applications without internet connectivity
• Debugging AI Behaviors: Inspect and debug model responses with full control
• CI/CD Integration: Include local model testing in your development pipeline

Example Use Case: A software team can develop and test AI-powered features locally before deploying to production, ensuring consistent behavior.

5. Educational and Research Applications 📚

• Teaching AI Concepts: Demonstrate how language models work without cloud dependencies
• Research Experiments: Conduct reproducible AI research with full control over models
• Student Projects: Enable students to work with powerful models on limited budgets
• Academic Papers: Generate consistent, reproducible results for publications

Example Use Case: A university professor can demonstrate different model behaviors and parameters to students in real-time during lectures.

6. Creative and Content Generation 🎨

• Consistent Character Voices: Maintain character consistency in creative writing
• Specialized Content: Generate technical documentation, marketing copy, or code
• Batch Processing: Process large volumes of content without API limits
• Style Transfer: Apply specific writing styles using fine-tuned models

Example Use Case: A content agency can generate initial drafts for multiple clients using specialized models for each client's brand voice.

7. Real-Time and Edge Applications ⚡

• Low Latency Requirements: Eliminate network latency for time-critical applications
• Offline Capabilities: Build applications that work without internet
• Edge Deployment: Run AI on edge devices or local servers
• Predictable Performance: Consistent response times without cloud variability

Example Use Case: A manufacturing plant can use local AI for real-time quality control without depending on internet connectivity.

8. Cost Optimization 💰

• No API Costs: Eliminate per-token pricing for high-volume applications
• Predictable Expenses: Fixed hardware costs instead of variable API fees
• Unlimited Usage: No rate limits or quotas
• Resource Control: Scale up or down based on your needs

Example Use Case: A startup can prototype and validate AI features without incurring significant API costs during development.

9. Custom Tool Integration 🛠️

• Database Queries: Connect directly to local databases
• File System Access: Process local files and directories
• System Integration: Interact with local applications and services
• Hardware Control: Interface with IoT devices or specialized hardware

Example Use Case: An automation engineer can use Claude Desktop with local Llama to control and monitor industrial equipment through custom tools.

10. Compliance and Audit Requirements 📋

• Complete Audit Trail: Log every interaction and model response
• Version Control: Pin specific model versions for reproducibility
• Data Governance: Maintain complete control over data flow
• Regulatory Compliance: Meet GDPR, HIPAA, or other regulatory requirements

Example Use Case: A healthcare provider can maintain HIPAA compliance while using AI for patient data analysis.

🎯 Overview

The Llama 4 Maverick MCP Server transforms your local Llama installation into a powerful backend service that can be accessed through MCP-compatible clients. This enables you to leverage the full power of local AI while maintaining the sophisticated interface and capabilities of Claude Desktop.

Architecture Overview

graph TB
    subgraph "Your Local Environment"
        A[Claude Desktop] -->|MCP Protocol| B[MCP Server]
        B --> C[Llama Service]
        C --> D[Ollama Runtime]
        D --> E[Llama Models]
        B --> F[Tool System]
        F --> G[File System]
        F --> H[Code Executor]
        F --> I[Web Search]
        F --> J[Database]
        B --> K[Prompt Manager]
        B --> L[Resource Manager]
    end
    
    subgraph "Benefits"
        M[Privacy: Data Never Leaves Your Machine]
        N[Control: Full Model Configuration]
        O[Cost: No API Fees]
        P[Speed: No Network Latency]
    end

🚀 Key Features

Core Capabilities

• ✅ Multiple Model Support: Switch between Llama 3, Llama 2, CodeLlama, and more
• ✅ Streaming Responses: Real-time token generation for better UX
• ✅ Function Calling: Native tool use with automatic parameter validation
• ✅ Rich Tool Ecosystem: 10+ built-in tools, easily extensible
• ✅ Prompt Management: Template system for consistent interactions
• ✅ Resource Access: Serve documentation, examples, and data
• ✅ Comprehensive Logging: Debug and monitor all operations
• ✅ Error Recovery: Graceful handling of failures

Advanced Features

• 🚀 Hot Reload: Change configurations without restarting
• 🚀 Model Caching: Faster response times after warm-up
• 🚀 Parallel Processing: Handle multiple requests simultaneously
• 🚀 Memory Management: Optimize context window usage
• 🚀 Custom Middleware: Add your own processing layers
• 🚀 Metrics Collection: Monitor performance and usage

💻 System Requirements

Minimum Requirements

Model Requirements by Size

📦 Complete Installation Guide

Quick Start (5 minutes)

# Clone the repository
git clone https://github.com/yobieben/llama4-maverick-mcp.git
cd llama4-maverick-mcp

# Run the automated setup
./quickstart.sh

# Start the server
./start.sh

Detailed Installation

Step 1: Install Prerequisites

Node.js Installation:

macOS:

# Using Homebrew
brew install node

# Or using MacPorts
sudo port install nodejs18

Windows:

# Using Chocolatey
choco install nodejs

# Or download from https://nodejs.org

Linux:

# Ubuntu/Debian
curl -fsSL https://deb.nodesource.com/setup_lts.x | sudo -E bash -
sudo apt-get install -y nodejs

# Fedora/RHEL
sudo dnf install nodejs

# Arch
sudo pacman -S nodejs npm

Step 2: Install Ollama

Automated Installation:

# macOS and Linux
curl -fsSL https://ollama.com/install.sh | sh

# Windows - Download from:
# https://ollama.com/download/windows

Manual Installation:

macOS:

brew install ollama

Linux:

# Download binary
wget https://github.com/ollama/ollama/releases/latest/download/ollama-linux-amd64
chmod +x ollama-linux-amd64
sudo mv ollama-linux-amd64 /usr/local/bin/ollama

Step 3: Set Up the MCP Server

# Clone repository
git clone https://github.com/yobieben/llama4-maverick-mcp.git
cd llama4-maverick-mcp

# Install dependencies
npm install

# Build the project
npm run build

# Configure environment
cp .env.example .env
# Edit .env with your preferences

Step 4: Download Llama Models

# Start Ollama service
ollama serve

# In a new terminal, pull models
ollama pull llama3:latest        # 8B model, recommended
ollama pull codellama:latest     # For code generation
ollama pull llama3:70b           # Large model (requires 48GB+ RAM)
ollama pull tinyllama:latest     # Tiny model for testing

# List available models
ollama list

Step 5: Configure Claude Desktop

Add to Claude Desktop configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "llama4-maverick": {
      "command": "node",
      "args": ["/absolute/path/to/llama4-maverick-mcp/dist/index.js"],
      "env": {
        "NODE_ENV": "production",
        "LLAMA_MODEL_NAME": "llama3:latest"
      }
    }
  }
}

Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "llama4-maverick": {
      "command": "node",
      "args": ["C:\\path\\to\\llama4-maverick-mcp\\dist\\index.js"],
      "env": {
        "NODE_ENV": "production",
        "LLAMA_MODEL_NAME": "llama3:latest"
      }
    }
  }
}

⚙️ Configuration Deep Dive

Environment Variables

Create a .env file with your configuration:

# ============================================
# OLLAMA CONFIGURATION
# ============================================
# Ollama API endpoint
LLAMA_API_URL=http://localhost:11434

# Model selection (examples below)
LLAMA_MODEL_NAME=llama3:latest
# LLAMA_MODEL_NAME=llama3:70b
# LLAMA_MODEL_NAME=codellama:latest
# LLAMA_MODEL_NAME=your-custom-model:latest

# Optional API key for secured Ollama instances
LLAMA_API_KEY=

# ============================================
# MCP SERVER CONFIGURATION
# ============================================
# Server settings
MCP_SERVER_PORT=3000
MCP_SERVER_HOST=localhost
MCP_LOG_LEVEL=info  # debug, info, warn, error

# Security
MCP_ENABLE_AUTH=false
MCP_API_KEY=your_secret_key_here

# ============================================
# PERFORMANCE TUNING
# ============================================
# Maximum context window (tokens)
MAX_CONTEXT_LENGTH=128000

# Concurrent request handling
MAX_CONCURRENT_REQUESTS=10

# Request timeout (milliseconds)
REQUEST_TIMEOUT_MS=30000

# ============================================
# FEATURE FLAGS
# ============================================
# Enable/disable features
ENABLE_STREAMING=true
ENABLE_FUNCTION_CALLING=true
ENABLE_VISION=false  # For future Llama vision models
ENABLE_CODE_EXECUTION=false  # Security risk - enable with caution

# ============================================
# MODEL PARAMETERS
# ============================================
# Generation parameters
TEMPERATURE=0.7      # 0.0 (deterministic) to 2.0 (creative)
TOP_P=0.9           # Nucleus sampling threshold
TOP_K=40            # Top-k sampling
REPEAT_PENALTY=1.1  # Penalize repetitions
SEED=42             # For reproducible outputs

# ============================================
# FILE SYSTEM
# ============================================
# Base path for file operations
FILE_SYSTEM_BASE_PATH=/Users/yourusername/Documents

# ============================================
# DATABASE (Optional)
# ============================================
DATABASE_URL=postgresql://localhost/mydb
DATABASE_POOL_SIZE=10

Model Selection Guide

🛠️ Available Tools

Built-in Tools Reference

Creating Custom Tools

Add your own tools by creating a new file in src/tools/:

// src/tools/my-custom-tool.ts
import { z } from 'zod';

export class MyCustomTool {
  name = 'my_custom_tool';
  description = 'Does something custom';
  
  inputSchema = z.object({
    param1: z.string().describe('First parameter'),
    param2: z.number().optional().describe('Optional number'),
  });
  
  async execute(args: any) {
    // Your tool logic here
    return {
      success: true,
      result: `Processed ${args.param1}`,
    };
  }
}

🧪 Usage & Experimentation Guide

Basic Usage Examples

1. Simple Chat:

User: "Hello, can you help me understand recursion?"
Llama (via MCP): "I'd be happy to explain recursion! Recursion is..."

2. Tool Use:

User: "Calculate the compound interest on $10,000 at 5% for 10 years"
Llama (via MCP): "I'll calculate that for you using the calculator tool..."
[Executes calculator tool]
Result: $16,288.95

3. File Operations:

User: "Read the package.json file and tell me the version"
Llama (via MCP): "I'll read that file for you..."
[Executes read_file tool]
"The version is 1.0.0"

Advanced Experimentation

1. Model Comparison:

// Compare responses from different models
const models = ['llama3:7b', 'llama3:13b', 'codellama:latest'];
for (const model of models) {
  process.env.LLAMA_MODEL_NAME = model;
  // Test same prompt with each model
}

2. Parameter Tuning:

// Experiment with generation parameters
const configs = [
  { temperature: 0.1, top_p: 0.9 },  // Focused
  { temperature: 0.7, top_p: 0.9 },  // Balanced
  { temperature: 1.5, top_p: 0.95 }, // Creative
];

3. Custom Prompts:

// Add to src/prompts/prompt-manager.ts
{
  name: 'code_reviewer',
  description: 'Review code for best practices',
  template: `Review this code for:
    1. Security issues
    2. Performance problems
    3. Best practices
    Code: {{code}}`,
}

📊 Real-World Examples

Example 1: Local Document Analysis System

// Analyze confidential documents without cloud exposure
const analyzer = {
  async analyzeDocument(filepath: string) {
    // Read document locally
    const content = await toolManager.execute('read_file', { path: filepath });
    
    // Analyze with Llama
    const analysis = await llamaService.complete({
      prompt: `Analyze this document for key insights: ${content}`,
      temperature: 0.3, // Low temperature for factual analysis
    });
    
    // Save results locally
    await toolManager.execute('write_file', {
      path: 'analysis_results.json',
      content: JSON.stringify(analysis),
    });
  }
};

Example 2: Code Generation Pipeline

// Generate and test code locally
const codeGenerator = {
  async generateAndTest(requirements: string) {
    // Generate code with CodeLlama
    process.env.LLAMA_MODEL_NAME = 'codellama:latest';
    const code = await llamaService.complete({
      prompt: `Generate Python code for: ${requirements}`,
    });
    
    // Test the generated code
    const result = await toolManager.execute('execute_code', {
      language: 'python',
      code: code,
      timeout: 5000,
    });
    
    return { code, testResult: result };
  }
};

Example 3: Custom Knowledge Base

// Build a local RAG system
const knowledgeBase = {
  async queryKnowledge(question: string) {
    // Search local documents
    const files = await toolManager.execute('list_files', {
      path: './knowledge_base',
      recursive: true,
    });
    
    // Read relevant files
    const contents = await Promise.all(
      files.map(f => toolManager.execute('read_file', { path: f }))
    );
    
    // Generate answer with context
    return await llamaService.complete({
      prompt: `Context: ${contents.join('\n')}\nQuestion: ${question}`,
      temperature: 0.3,
    });
  }
};

🚀 Performance Optimization

Speed Optimization Tips

1. Model Selection:

   • Use smaller models for simple tasks
   • Reserve large models for complex reasoning
   • Consider quantized models for better speed/quality trade-off

2. Caching Strategy:

   // Enable response caching
   const cache = new Map();
   
   async function cachedComplete(prompt: string) {
     const key = hash(prompt);
     if (cache.has(key)) return cache.get(key);
     
     const result = await llamaService.complete({ prompt });
     cache.set(key, result);
     return result;
   }
   

3. Batch Processing:

   // Process multiple requests in parallel
   const results = await Promise.all(
     prompts.map(p => llamaService.complete({ prompt: p }))
   );
   

4. Context Management:

   // Optimize context window usage
   function truncateContext(text: string, maxTokens: number = 4096) {
     // Smart truncation preserving important information
     return text.slice(-maxTokens * 4); // Approximate
   }
   

Memory Optimization

1. Model Loading:

   # Load models on demand
   ollama run llama3:latest --keep-alive 0  # Unload when idle
   

2. Garbage Collection:

   // Force garbage collection (if --expose-gc flag is set)
   if (global.gc) {
     setInterval(() => {
       global.gc();
     }, 60000); // Every minute
   }
   

3. Stream Processing:

   // Use streaming to reduce memory footprint
   const stream = await llamaService.complete({
     prompt: "Large prompt...",
     stream: true,
   });
   
   for await (const chunk of stream) {
     process.stdout.write(chunk);
   }
   

🔧 Troubleshooting

Common Issues and Solutions

Debug Mode

Enable comprehensive debugging:

# In .env file
MCP_LOG_LEVEL=debug
NODE_ENV=development

# Run with debug output
DEBUG=* npm run dev

# Check logs
tail -f logs/combined.log
tail -f logs/error.log

Health Checks

// Add health check endpoint
async function healthCheck() {
  const checks = {
    ollama: await checkOllama(),
    model: await checkModel(),
    tools: toolManager.getTools().length > 0,
    memory: process.memoryUsage(),
  };
  
  return {
    status: Object.values(checks).every(v => v) ? 'healthy' : 'degraded',
    checks,
  };
}

🔒 Security Best Practices

1. Secure Configuration

# Use environment variables for sensitive data
MCP_API_KEY=$(openssl rand -hex 32)
MCP_ENABLE_AUTH=true

# Restrict file access
FILE_SYSTEM_BASE_PATH=/safe/directory
ENABLE_CODE_EXECUTION=false  # Unless absolutely needed

2. Network Security

// Implement rate limiting
const rateLimit = new Map();

function checkRateLimit(clientId: string): boolean {
  const requests = rateLimit.get(clientId) || 0;
  if (requests > 100) return false; // 100 requests per minute
  rateLimit.set(clientId, requests + 1);
  return true;
}

3. Input Validation

// Validate all inputs
const SafePathSchema = z.string().refine(
  path => !path.includes('..') && path.startsWith(BASE_PATH),
  'Invalid path'
);

4. Audit Logging

// Log all operations for audit
function auditLog(operation: string, user: string, details: any) {
  logger.info('AUDIT', {
    timestamp: new Date().toISOString(),
    operation,
    user,
    details,
  });
}

🤝 Contributing

We welcome contributions! Here's how to get started:

Development Setup

# Fork and clone
git clone https://github.com/YOUR_USERNAME/llama4-maverick-mcp.git
cd llama4-maverick-mcp

# Install dev dependencies
npm install

# Run in development mode
npm run dev

# Run tests
npm test

# Lint and format
npm run lint
npm run format

Contribution Guidelines

1. Code Style: Follow TypeScript best practices
2. Documentation: Update README for new features
3. Tests: Add tests for new functionality
4. Commits: Use conventional commits
5. Pull Requests: Describe changes clearly

Areas for Contribution

• 🛠️ New tools and integrations
• 📝 Documentation improvements
• 🐛 Bug fixes
• 🚀 Performance optimizations
• 🧪 Test coverage
• 🌐 Internationalization

📄 License

MIT License - See file

👨‍💻 Author

Yobie Benjamin
Version 0.9
August 1, 2025

🙏 Acknowledgments

• Anthropic for the MCP protocol specification
• Ollama team for local model hosting
• Meta for Llama models
• Open source community for invaluable contributions

📞 Support

• GitHub Issues: Report bugs or request features
• Discussions: Community forum
• Documentation: Full docs

Ready to revolutionize your AI workflow? Start experimenting with Llama 4 Maverick MCP today! 🦙🚀