Ollama instance and routes appropriate requests 2. **MCP Protocol Handlers**: Translate between standard MCP format and Ollama's requirements 3. **Service Modules**: Independent modules that implement specific capabilities 4. **Client Library**: Provides a unified interface for applications to interact with the system This architecture provides several benefits: - **Scalability**: Add new modules without affecting existing ones - **Resilience**: System continues functioning even if individual modules fail - **Flexibility**: Deploy only the components you need - **Security**: Granular control over data access for each module ## 📚 Documentation ### Module-Specific Documentation Each module contains its own README with detailed implementation notes: - [Calendar Module Documentation](calendar/README.md) - [Client MCP Documentation](client_mcp/README.md) - [File System Documentation](file_system/README.md) ## 🛠️ Use Cases ### Enterprise Security & Compliance Ideal for organizations that need AI capabilities but face strict data sovereignty requirements: - Legal firms processing confidential case files - Healthcare providers analyzing patient data - Financial institutions handling sensitive transactions ### Developer Productivity Transform your local development environment: - Code generation with access to your project files - Automated documentation based on codebase analysis - Integration with local git repositories ### Personal Knowledge Management Create a powerful second brain that respects your privacy: - Process personal documents and notes - Manage calendar and schedule optimization - Generate content based on your personal knowledge base ## 🤝 Contributing We welcome contributions from the community! Here's how you can help: 1. **Fork the Repository**: Create your own fork of the project 2. **Create a Feature Branch**: `git checkout -b feature/amazing-feature` 3. **Make Your Changes**: Implement your feature or bug fix 4. **Run Tests**: Ensure your changes pass all tests 5. **Commit Changes**: `git commit -m 'Add some amazing feature'` 6. **Push to Branch**: `git push origin feature/amazing-feature` 7. **Open a Pull Request**: Submit your changes for review Please read our [Contributing Guidelines](CONTRIBUTING.md) for more details. ## ❓ FAQ **Q: How does this differ from using cloud-based AI assistants?** A: MCP-Ollama Server runs entirely on your local infrastructure, ensuring complete data privacy and eliminating dependence on external APIs. **Q: What models are supported?** A: Any model compatible with Ollama can be used. For best results, we recommend Llama 3, Mistral, or other recent open models with at least 7B parameters. **Q: How can I extend the system with new capabilities?** A: Follow the modular architecture pattern to create new service modules. See our [Extension Guide](docs/extension.md) for details. **Q: What are the system requirements?** A: Requirements depend on the Ollama model you choose. For basic functionality, we recommend at least 16GB RAM and a modern multi-core CPU. ## 📄 License This project is licensed under the terms included in the [LICENSE](LICENSE) file. ## 🙏 Acknowledgements - [Anthropic](https://www.anthropic.com/) for the Model Context Protocol specification - [Ollama](https://ollama.ai/) for their excellent local LLM server ---

MCP-Ollama Server - Bringing cloud-level AI capabilities to your local environment

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2a:T69d,## What is MCP-Ollama Server? MCP-Ollama Server integrates the Model Context Protocol (MCP) with local LLMs via Ollama, enabling advanced tool use while ensuring data privacy. It allows users to leverage AI capabilities similar to Claude, including file access,

🚀 MCP-Ollama Server

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