This article details how to build powerful, local AI automations using n8n, the Model Context Protocol (MCP), and Ollama, aiming to replace fragile scripts and expensive cloud-based APIs. These tools work together to automate tasks like log triage, data quality monitoring, dataset labeling, research brief updates, incident postmortems, contract review, and code review – all while keeping data and processing local for enhanced control and efficiency.
**Key Points:**
* **Local Focus:** The system prioritizes running LLMs locally for speed, cost-effectiveness, and data privacy.
* **Component Roles:** n8n orchestrates workflows, MCP constrains tool usage, and Ollama provides reasoning capabilities.
* **Automation Examples:** The article showcases several practical automation examples across various domains, from DevOps to legal compliance.
* **Controlled Access:** MCP limits the model's access to only necessary tools and data, enhancing security and reliability.
* **Closed-Loop Systems:** Many automations incorporate feedback loops for continuous improvement and reduced human intervention.
**Key Points:**
* **Local Focus:** The system prioritizes running LLMs locally for speed, cost-effectiveness, and data privacy.
* **Component Roles:** n8n orchestrates workflows, MCP constrains tool usage, and Ollama provides reasoning capabilities.
* **Automation Examples:** The article showcases several practical automation examples across various domains, from DevOps to legal compliance.
* **Controlled Access:** MCP limits the model's access to only necessary tools and data, enhancing security and reliability.
* **Closed-Loop Systems:** Many automations incorporate feedback loops for continuous improvement and reduced human intervention.
This article details seven pre-built n8n workflows designed to streamline common data science tasks, including data extraction, cleaning, model training, and deployment.
