Model Context Protocol (MCP)
How Model Context Protocol (MCP) Works in Production
Prior to MCP, every AI agent framework required custom, fragile API wrappers to interact with databases, GitHub repos, Slack channels, or local file systems. MCP introduces a client-server architecture using JSON-RPC over stdio or SSE. An AI assistant acts as an MCP client connecting to modular MCP servers, enabling portable, permission-gated tool execution across tools and clouds.
Practical Enterprise Workflow
An internal engineering assistant uses an MCP PostgreSQL server to securely inspect read-only schema definitions and an MCP Linear server to file engineering tickets without custom glue code.
Related Engineering Practices
Related Glossary Terms
About Aurevion AI
Custom AI systems, private RAG architectures, and sovereign software development for growing engineering teams.
Aurevion AI engineers dependable production artificial intelligence systems, sovereign retrieval-augmented generation pipelines, autonomous agentic workflows, and modern high-concurrency software applications. We partner directly with engineering leaders across North America and Europe, deploying zero-trust private cloud architectures with complete intellectual property ownership, deterministic reliability guarantees, and transparent sprint-based technical team collaboration.