🦙 Llama 4 Maverick MCP Server (Python)

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

A Python implementation of the Model Context Protocol (MCP) server that bridges Llama models with Claude Desktop through Ollama. This pure Python solution offers clean architecture, high performance, and easy extensibility.

📚 Table of Contents

🎯 What Would You Use This Llama MCP Server For?

The Revolution of Local AI + Claude Desktop

This Python MCP server creates a powerful bridge between Claude Desktop's sophisticated interface and your locally-hosted Llama models. Here's what makes this combination revolutionary:

1. Privacy-First AI Operations 🔒

The Challenge: Organizations handling sensitive data can't use cloud AI due to privacy concerns.

The Solution: This MCP server keeps everything local while providing enterprise-grade AI capabilities.

Real-World Applications:

Healthcare: A hospital can analyze patient records using AI without violating HIPAA compliance
Legal: Law firms can process confidential client documents with complete privacy
Finance: Banks can analyze transaction data without exposing customer information
Government: Agencies can process classified documents on air-gapped systems

Example Implementation:

# Process sensitive medical records locally async def analyze_patient_data(patient_file): # Data never leaves your server content = await tool_manager.execute("read_file", {"path": patient_file}) # Use specialized medical model analysis = await llama_service.complete( prompt=f"Analyze patient data for risk factors: {content}", model="medical-llama:latest", # Your HIPAA-compliant fine-tuned model temperature=0.1 # Low temperature for medical accuracy ) # Store results locally with encryption await secure_storage.save(analysis, encrypted=True)

2. Custom Model Deployment 🎯

The Challenge: Generic models don't understand your domain-specific language and requirements.

The Solution: Deploy your own fine-tuned models through the MCP interface.

Real-World Applications:

Research Labs: Use models trained on proprietary research data
Enterprises: Deploy models fine-tuned on company documentation
Educational Institutions: Use models trained on curriculum-specific content
Industry-Specific: Legal, medical, financial, or technical domain models

Example Implementation:

# Switch between specialized models based on task class ModelSelector: def __init__(self): self.models = { "general": "llama3:latest", "code": "codellama:latest", "medical": "medical-llama:13b", "legal": "legal-llama:7b", "finance": "finance-llama:13b" } async def select_and_query(self, domain: str, prompt: str): model = self.models.get(domain, "llama3:latest") return await llama_service.complete( prompt=prompt, model=model, temperature=0.3 if domain in ["medical", "legal"] else 0.7 )

3. Hybrid Intelligence Systems 🔄

The Challenge: No single AI model excels at everything.

The Solution: Combine Claude's reasoning with Llama's generation capabilities.

Real-World Applications:

Software Development: Claude plans architecture, Llama generates implementation
Content Creation: Claude creates outlines, Llama writes detailed content
Data Analysis: Claude interprets results, Llama generates reports
Research: Claude formulates hypotheses, Llama explores implications

Example Implementation:

# Hybrid workflow combining Claude and Llama class HybridAI: async def complex_task(self, requirement: str): # Step 1: Use Claude for high-level planning plan = await claude.create_plan(requirement) # Step 2: Use local Llama for detailed implementation implementation = await llama_service.complete( prompt=f"Implement this plan: {plan}", model="codellama:34b", max_tokens=4096 ) # Step 3: Use Claude for review and refinement refined = await claude.review_and_refine(implementation) return refined

4. Offline and Edge Computing 🌐

The Challenge: Many environments lack reliable internet or prohibit cloud connections.

The Solution: Full AI capabilities without any internet requirement.

Real-World Applications:

Remote Operations: Oil rigs, ships, remote research stations
Industrial IoT: Factory floors with real-time requirements
Field Work: Geological surveys, wildlife research, disaster response
Secure Facilities: Military bases, research labs, government buildings

Example Implementation:

# Edge deployment for industrial quality control class EdgeQualityControl: def __init__(self): self.config = Config( llama_model_name="quality-control:latest", enable_streaming=True, max_context_length=8192 # Optimized for edge devices ) async def inspect_product(self, sensor_data: dict): # Process sensor data locally analysis = await llama_service.complete( prompt=f"Analyze sensor readings for defects: {sensor_data}", temperature=0.1, # Consistent results needed max_tokens=256 # Quick response for real-time processing ) # Trigger local actions based on analysis if "defect" in analysis.lower(): await self.trigger_alert(analysis) return analysis

5. Experimentation and Research 🧪

The Challenge: Researchers need reproducible results and full control over model behavior.

The Solution: Complete transparency and control over every aspect of the AI pipeline.

Real-World Applications:

Academic Research: Reproducible experiments for papers
Model Comparison: A/B testing different models and parameters
Behavior Analysis: Understanding how models respond to different inputs
Prompt Engineering: Developing optimal prompts for specific tasks

Example Implementation:

# Research experiment framework class ExperimentRunner: async def run_experiment(self, hypothesis: str, test_cases: list): results = [] # Test multiple models for model in ["llama3:7b", "llama3:13b", "llama3:70b"]: # Test multiple parameters for temp in [0.1, 0.5, 0.9, 1.5]: model_results = [] for test in test_cases: response = await llama_service.complete( prompt=test, model=model, temperature=temp, seed=42 # Reproducible results ) model_results.append({ "input": test, "output": response, "model": model, "temperature": temp, "timestamp": datetime.now() }) results.append(model_results) # Analyze and save results analysis = self.analyze_results(results) await self.save_experiment(hypothesis, results, analysis) return analysis

6. Cost-Effective Scaling 💰

The Challenge: API costs can become prohibitive for high-volume applications.

The Solution: One-time hardware investment for unlimited usage.

Real-World Applications:

Startups: Prototype without burning through funding
Education: Provide AI access to all students without budget concerns
Non-profits: Leverage AI without ongoing costs
High-volume Processing: Batch jobs, data analysis, content generation

Cost Analysis Example:

# Cost comparison calculator class CostAnalyzer: def calculate_savings(self, monthly_tokens: int): # API costs (approximate) api_cost_per_million = 15.00 # USD monthly_api_cost = (monthly_tokens / 1_000_000) * api_cost_per_million # Local costs (one-time hardware) hardware_cost = 2000 # Good GPU setup electricity_monthly = 50 # Approximate # Calculate break-even months_to_break_even = hardware_cost / (monthly_api_cost - electricity_monthly) return { "monthly_api_cost": monthly_api_cost, "monthly_local_cost": electricity_monthly, "monthly_savings": monthly_api_cost - electricity_monthly, "break_even_months": months_to_break_even, "first_year_savings": (monthly_api_cost * 12) - (hardware_cost + electricity_monthly * 12) }

7. Real-Time Processing ⚡

The Challenge: Network latency makes cloud AI unsuitable for real-time applications.

The Solution: Sub-second response times with local processing.

Real-World Applications:

Trading Systems: Analyze market data in milliseconds
Gaming: Real-time NPC dialogue and behavior
Robotics: Immediate response to sensor inputs
Live Translation: Instant language translation

Example Implementation:

# Real-time stream processing class StreamProcessor: def __init__(self): self.buffer = [] self.processing = False async def process_stream(self, data_stream): async for chunk in data_stream: self.buffer.append(chunk) if not self.processing and len(self.buffer) > 0: self.processing = True # Process immediately without network delay result = await llama_service.complete( prompt=f"Analyze: {self.buffer[-1]}", model="tinyllama:latest", # Fast model for real-time max_tokens=50, stream=True ) async for token in result: yield token # Stream results immediately self.processing = False

8. Custom Tool Integration 🛠️

The Challenge: Generic AI can't interact with your specific systems and databases.

The Solution: Build custom tools that integrate with your infrastructure.

Real-World Applications:

DevOps: AI that can manage your specific infrastructure
Database Management: Query and manage your databases via natural language
System Administration: Automate complex administrative tasks
Business Intelligence: Connect to your BI tools and data warehouses

Example Implementation:

# Custom tool for database operations class DatabaseTool(BaseTool): @property def name(self) -> str: return "company_database" @property def description(self) -> str: return "Query and manage company database" async def execute(self, query: str, operation: str = "select") -> ToolResult: # Connect to your specific database async with get_company_db() as db: if operation == "select": results = await db.fetch(query) return ToolResult(success=True, data=results) elif operation == "analyze": # Use Llama to analyze query results analysis = await llama_service.complete( prompt=f"Analyze this data: {results}", temperature=0.3 ) return ToolResult(success=True, data=analysis)

9. Compliance and Governance 📋

The Challenge: Regulatory requirements demand complete control and audit trails.

The Solution: Full transparency and logging of all AI operations.

Real-World Applications:

Healthcare: HIPAA compliance with audit trails
Finance: SOX compliance with transaction monitoring
Legal: Attorney-client privilege protection
Government: Security clearance requirements

Example Implementation:

# Compliance-aware AI system class ComplianceAI: def __init__(self): self.audit_logger = AuditLogger() self.encryption = EncryptionService() async def process_regulated_data(self, data: str, user: str, purpose: str): # Log access for audit audit_id = await self.audit_logger.log_access( user=user, data_type="regulated", purpose=purpose, timestamp=datetime.now() ) # Encrypt data in transit encrypted = self.encryption.encrypt(data) # Process with local model (data never leaves premises) result = await llama_service.complete( prompt=f"Process: {encrypted}", model="compliance-llama:latest" ) # Log completion await self.audit_logger.log_completion( audit_id=audit_id, success=True, result_hash=hashlib.sha256(result.encode()).hexdigest() ) return self.encryption.encrypt(result)

10. Educational Environments 🎓

The Challenge: Educational institutions need affordable AI access for all students.

The Solution: Single deployment serves unlimited students without per-use costs.

Real-World Applications:

Computer Science: Teaching AI/ML concepts hands-on
Research Projects: Student research without budget constraints
Writing Centers: AI-assisted writing for all students
Language Learning: Personalized language practice

Example Implementation:

# Educational AI assistant class EducationalAssistant: def __init__(self): self.student_profiles = {} self.learning_analytics = LearningAnalytics() async def personalized_tutoring(self, student_id: str, subject: str, question: str): # Get student's learning profile profile = self.student_profiles.get(student_id, self.create_profile(student_id)) # Adjust response based on student level response = await llama_service.complete( prompt=f""" Student Level: {profile['level']} Subject: {subject} Question: {question} Provide an explanation appropriate for this student's level. """, temperature=0.7, model="education-llama:latest" ) # Track learning progress await self.learning_analytics.record_interaction( student_id=student_id, subject=subject, question=question, response=response ) return response

🐍 Why Python?

Advantages Over TypeScript/Node.js

Aspect	Python Advantage	Use Case
Scientific Computing	NumPy, SciPy, Pandas integration	Data analysis, research
ML Ecosystem	Direct integration with PyTorch, TensorFlow	Model experimentation
Simplicity	Cleaner async/await syntax	Faster development
Libraries	Vast ecosystem of AI/ML tools	Extended functionality
Debugging	Better error messages and debugging tools	Easier troubleshooting
Performance	uvloop for high-performance async	Better concurrency
Type Safety	Type hints + Pydantic validation	Runtime validation

✨ Features

Core Capabilities

🚀 High Performance: Async/await with uvloop support
🛠️ 10+ Built-in Tools: Web search, file ops, calculations, and more
📝 Prompt Templates: Pre-defined prompts for common tasks
📁 Resource Management: Access templates and documentation
🔄 Streaming Support: Real-time token generation
🔧 Highly Configurable: Environment-based configuration
📊 Structured Logging: Comprehensive debugging support
🧪 Fully Tested: Pytest test suite included

Python-Specific Features

🐼 Data Science Integration: Works with Pandas, NumPy
🤖 ML Framework Compatible: Integrate with PyTorch, TensorFlow
📈 Analytics Built-in: Performance metrics and monitoring
🔌 Plugin System: Easy to extend with Python packages
🎯 Type Safety: Pydantic models for validation
🔒 Security: Built-in sanitization and validation

💻 System Requirements

Minimum Requirements

Component	Minimum	Recommended	Optimal
Python	3.9+	3.11+	Latest
CPU	4 cores	8 cores	16+ cores
RAM	8GB	16GB	32GB+
Storage	10GB SSD	50GB SSD	100GB NVMe
OS	Linux/macOS/Windows	Ubuntu 22.04	Latest Linux

Model Requirements

Model	Parameters	RAM	Use Case
`tinyllama`	1.1B	2GB	Testing, quick responses
`llama3:7b`	7B	8GB	General purpose
`llama3:13b`	13B	16GB	Advanced tasks
`llama3:70b`	70B	48GB	Professional use
`codellama`	7-34B	8-32GB	Code generation

🚀 Quick Start

# Clone the repository git clone https://github.com/yobieben/llama4-maverick-mcp-python.git cd llama4-maverick-mcp-python # Run setup (handles everything) python setup.py # Start the server python -m llama4_maverick_mcp.server

That's it! The server is now running and ready to connect to Claude Desktop.

📦 Detailed Installation

Step 1: Python Setup

# Check Python version python --version # Should be 3.9+ # Create virtual environment (recommended) python -m venv venv # Activate virtual environment # Linux/macOS: source venv/bin/activate # Windows: venv\Scripts\activate

Step 2: Install Dependencies

# Install the package in development mode pip install -e . # For development with testing tools pip install -e .[dev]

Step 3: Install Ollama

# macOS brew install ollama # Linux curl -fsSL https://ollama.com/install.sh | sh # Windows # Download from https://ollama.com/download/windows

Step 4: Configure Environment

# Copy example configuration cp .env.example .env # Edit configuration nano .env # or your preferred editor

Step 5: Download Models

# Start Ollama service ollama serve # In another terminal, pull models ollama pull llama3:latest ollama pull codellama:latest ollama pull tinyllama:latest

Step 6: Configure Claude Desktop

Add to Claude Desktop configuration:

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

{ "mcpServers": { "llama4-python": { "command": "python", "args": ["-m", "llama4_maverick_mcp.server"], "cwd": "/path/to/llama4-maverick-mcp-python", "env": { "PYTHONPATH": "/path/to/llama4-maverick-mcp-python/src", "LLAMA_MODEL_NAME": "llama3:latest" } } } }

⚙️ Configuration

Environment Variables

Create a .env file:

# Ollama Configuration LLAMA_API_URL=http://localhost:11434 LLAMA_MODEL_NAME=llama3:latest LLAMA_API_KEY= # Optional # Server Configuration MCP_LOG_LEVEL=INFO MCP_SERVER_HOST=localhost MCP_SERVER_PORT=3000 # Features ENABLE_STREAMING=true ENABLE_FUNCTION_CALLING=true ENABLE_VISION=false ENABLE_CODE_EXECUTION=false # Security risk ENABLE_WEB_SEARCH=true # Model Parameters TEMPERATURE=0.7 # 0.0-2.0 TOP_P=0.9 # 0.0-1.0 TOP_K=40 # 1-100 REPEAT_PENALTY=1.1 SEED=42 # For reproducibility # File System FILE_SYSTEM_BASE_PATH=/safe/path ALLOW_FILE_WRITES=true # Performance MAX_CONTEXT_LENGTH=128000 MAX_CONCURRENT_REQUESTS=10 REQUEST_TIMEOUT_MS=30000 CACHE_TTL=3600 CACHE_MAX_SIZE=1000 # Debug DEBUG=false VERBOSE_LOGGING=false

Configuration Classes

from llama4_maverick_mcp.config import Config # Create custom configuration config = Config( llama_model_name="codellama:latest", temperature=0.3, enable_code_execution=True ) # Access configuration print(config.llama_model_name) print(config.get_model_params())

🛠️ Available Tools

Built-in Tools

Tool	Description	Example
`calculator`	Mathematical calculations	`2 + 2` , `sqrt(16)`
`datetime`	Date/time operations	Current time, date math
`json_tool`	JSON manipulation	Parse, extract, transform
`web_search`	Search the web	Query for information
`file_read`	Read files	Access local files
`file_write`	Write files	Save data locally
`list_files`	List directories	Browse file system
`code_executor`	Run code	Execute Python/JS/Bash
`http_request`	HTTP calls	API interactions

Creating Custom Tools

# src/llama4_maverick_mcp/tools/custom/my_tool.py from pydantic import BaseModel, Field from ..base import BaseTool, ToolResult class MyToolParams(BaseModel): """Parameters for my custom tool.""" input_text: str = Field(..., description="Text to process") option: str = Field(default="default", description="Processing option") class MyCustomTool(BaseTool): @property def name(self) -> str: return "my_custom_tool" @property def description(self) -> str: return "Performs custom processing on text" @property def parameters(self) -> type[BaseModel]: return MyToolParams async def execute(self, input_text: str, option: str = "default") -> ToolResult: # Your custom logic here result = f"Processed: {input_text} with option: {option}" return ToolResult( success=True, data={"result": result, "length": len(input_text)} )

📊 Usage Examples

Basic Usage

import asyncio from llama4_maverick_mcp import MCPServer, Config async def main(): # Create server with custom config config = Config( llama_model_name="llama3:latest", temperature=0.7 ) server = MCPServer(config) # Run the server await server.run() if __name__ == "__main__": asyncio.run(main())

Direct API Usage

from llama4_maverick_mcp import LlamaService, Config async def generate_text(): config = Config() llama = LlamaService(config) await llama.initialize() # Simple completion result = await llama.complete( prompt="Explain quantum computing", temperature=0.5, max_tokens=200 ) print(result) # Chat completion messages = [ {"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": "What is Python?"} ] response = await llama.complete_chat(messages) print(response)

Tool Execution

from llama4_maverick_mcp.tools import ToolManager async def use_tools(): manager = ToolManager(Config()) await manager.initialize() # Execute calculator result = await manager.execute_tool( "calculator", {"expression": "factorial(5) + sqrt(16)"} ) print(result) # Read file content = await manager.execute_tool( "file_read", {"path": "config.json"} ) print(content)

🌟 Real-World Applications

1. Document Analysis Pipeline

class DocumentAnalyzer: def __init__(self): self.config = Config(temperature=0.3) self.llama = LlamaService(self.config) self.tools = ToolManager(self.config) async def analyze_documents(self, directory: str): # List all documents files = await self.tools.execute_tool( "list_files", {"path": directory, "recursive": True} ) results = [] for file in files['data']['files']: if file.endswith(('.txt', '.md', '.pdf')): # Read document content = await self.tools.execute_tool( "file_read", {"path": file} ) # Analyze with Llama analysis = await self.llama.complete( prompt=f"Summarize and extract key points: {content['data']}", max_tokens=500 ) results.append({ "file": file, "analysis": analysis }) return results

2. Code Review System

class CodeReviewer: async def review_code(self, code: str, language: str = "python"): prompt = f""" Review this {language} code for: 1. Security vulnerabilities 2. Performance issues 3. Best practices 4. Potential bugs Code: ```{language} {code} ``` Provide specific suggestions for improvement. """ review = await llama_service.complete( prompt=prompt, model="codellama:latest", temperature=0.3 ) return self.parse_review(review)

3. Research Assistant

class ResearchAssistant: async def research_topic(self, topic: str): # Search for information search_results = await self.tools.execute_tool( "web_search", {"query": topic, "max_results": 10} ) # Analyze sources analysis = await self.llama.complete( prompt=f"Analyze these sources about {topic}: {search_results}", temperature=0.5 ) # Generate report report = await self.llama.complete( prompt=f"Write a comprehensive report on {topic} based on: {analysis}", temperature=0.7, max_tokens=2000 ) # Save report await self.tools.execute_tool( "file_write", { "path": f"research_{topic}_{datetime.now().strftime('%Y%m%d')}.md", "content": report } ) return report

🧪 Development

Running Tests

# Run all tests pytest # Run with coverage pytest --cov=llama4_maverick_mcp # Run specific test pytest tests/test_llama_service.py # Run with verbose output pytest -v

Code Quality

# Format code with Black black src/ # Lint with Ruff ruff check src/ # Type checking with mypy mypy src/ # All quality checks make quality

Creating Tests

# tests/test_my_tool.py import pytest from llama4_maverick_mcp.tools.custom.my_tool import MyCustomTool @pytest.mark.asyncio async def test_my_custom_tool(): tool = MyCustomTool() result = await tool.execute( input_text="Hello, world!", option="uppercase" ) assert result.success assert "Hello, world!" in result.data["result"] assert result.data["length"] == 13

🚀 Performance Optimization

1. Use uvloop (Linux/macOS)

# Automatically enabled if available # 2-4x performance improvement for async operations pip install uvloop

2. Model Optimization

# Use smaller models for simple tasks config = Config( llama_model_name="tinyllama:latest", # 1.1B params, very fast max_context_length=4096, # Reduce context for speed temperature=0.1 # Lower temperature for consistency )

3. Caching Strategy

from functools import lru_cache from cachetools import TTLCache class CachedLlamaService(LlamaService): def __init__(self, config): super().__init__(config) self.cache = TTLCache(maxsize=1000, ttl=3600) async def complete(self, prompt: str, **kwargs): cache_key = f"{prompt}:{kwargs}" if cache_key in self.cache: return self.cache[cache_key] result = await super().complete(prompt, **kwargs) self.cache[cache_key] = result return result

4. Batch Processing

import asyncio async def batch_process(prompts: list): # Process multiple prompts concurrently tasks = [ llama_service.complete(prompt, temperature=0.5) for prompt in prompts ] # Limit concurrency to avoid overwhelming the system semaphore = asyncio.Semaphore(5) async def limited_task(task): async with semaphore: return await task results = await asyncio.gather(*[limited_task(t) for t in tasks]) return results

🔧 Troubleshooting

Common Issues

Issue	Solution
ImportError	Check Python path: `export PYTHONPATH=$PYTHONPATH:$(pwd)/src`
Ollama not found	Install: `curl -fsSL https://ollama.com/install.sh \| sh`
Model not available	Pull model: `ollama pull llama3:latest`
Permission denied	Check file permissions and base path configuration
Memory error	Use smaller model or increase system RAM
Timeout errors	Increase `REQUEST_TIMEOUT_MS` in configuration

Debug Mode

# Enable detailed logging config = Config( debug_mode=True, verbose_logging=True, log_level="DEBUG" ) # Or via environment export DEBUG=true export MCP_LOG_LEVEL=DEBUG export VERBOSE_LOGGING=true

Health Check

async def health_check(): """Check system health.""" checks = { "python_version": sys.version, "ollama_connected": config.validate_ollama_connection(), "models_available": await llama_service.list_models(), "tools_loaded": len(await tool_manager.get_tools()), "memory_usage": psutil.virtual_memory().percent, "disk_usage": psutil.disk_usage('/').percent } return { "status": "healthy" if all(checks.values()) else "degraded", "checks": checks, "timestamp": datetime.now().isoformat() }

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Areas for Contribution

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

Development Workflow

# Fork and clone git clone https://github.com/YOUR_USERNAME/llama4-maverick-mcp-python.git # Create branch git checkout -b feature/your-feature # Make changes and test pytest # Commit with conventional commits git commit -m "feat: add new amazing feature" # Push and create PR git push origin feature/your-feature

📄 License

MIT License - See LICENSE file

👨‍💻 Author

Yobie Benjamin
Version 0.9
August 1, 2025

🙏 Acknowledgments

Anthropic for the MCP protocol
Ollama team for local model hosting
Meta for Llama models
Python community for excellent libraries

📞 Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Documentation: Wiki

Ready to experience the power of local AI? Start with Llama 4 Maverick MCP Python today! 🦙🐍🚀

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local-only server

The server can only run on the client's local machine because it depends on local resources.

Bridges Llama models with Claude Desktop through Ollama, enabling privacy-first local AI operations with 10+ built-in tools for file operations, web search, calculations, and custom model deployment. Features streaming support, hybrid intelligence workflows, and extensive Python ecosystem integration for research, development, and enterprise applications.

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🦙 Llama 4 Maverick MCP Server (Python)

📚 Table of Contents

🎯 What Would You Use This Llama MCP Server For?

The Revolution of Local AI + Claude Desktop

1. Privacy-First AI Operations 🔒

2. Custom Model Deployment 🎯

3. Hybrid Intelligence Systems 🔄

4. Offline and Edge Computing 🌐

5. Experimentation and Research 🧪

6. Cost-Effective Scaling 💰

7. Real-Time Processing ⚡

8. Custom Tool Integration 🛠️

9. Compliance and Governance 📋

10. Educational Environments 🎓

🐍 Why Python?

Advantages Over TypeScript/Node.js

✨ Features

Core Capabilities

Python-Specific Features

💻 System Requirements

Minimum Requirements

Model Requirements

🚀 Quick Start

📦 Detailed Installation

Step 1: Python Setup

Step 2: Install Dependencies

Step 3: Install Ollama

Step 4: Configure Environment

Step 5: Download Models

Step 6: Configure Claude Desktop

⚙️ Configuration

Environment Variables

Configuration Classes

🛠️ Available Tools

Built-in Tools

Creating Custom Tools

📊 Usage Examples

Basic Usage

Direct API Usage

Tool Execution

🌟 Real-World Applications

1. Document Analysis Pipeline

2. Code Review System

3. Research Assistant

🧪 Development

Running Tests

Code Quality

Creating Tests

🚀 Performance Optimization

1. Use uvloop (Linux/macOS)

2. Model Optimization

3. Caching Strategy

4. Batch Processing

🔧 Troubleshooting

Common Issues

Debug Mode

Health Check

🤝 Contributing

Areas for Contribution

Development Workflow

📄 License

👨‍💻 Author

🙏 Acknowledgments

📞 Support

Related MCP Servers

Ollama MCP Server

openai-tool2mcp

LMStudio-MCP

Zen MCP Server

New MCP Servers

MCP directory API