Model Context Protocol (MCP): Building a Scalable Multi-Cloud LLM Service

This article is part of the [Full-Stack DevOps Cloud AI Complete Handbook](https://github.com/prodxcloud/fullstack-devops-cloud-ai-complete-handbook/), focusing on implementing Anthropic’s Model Context Protocol for enhanced LLM interactions. Model Context Protocol, MCP, LLaMA 2, context management, prompt engineering, multi-cloud deployment

Model Context Protocol (MCP): Building a Scalable Multi-Cloud LLM Service

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The Model Context Protocol (MCP), introduced by Anthropic, represents a standardized approach to managing context in large language models. Our implementation focuses on:

1. Context Management: Structured handling of conversation history and system prompts

2. Protocol Standardization: Consistent format for model interactions

3. Context Window Optimization: Efficient use of available context space

4. Cross-Model Compatibility: Standardized interactions across different LLMs