In software development, context engineering is the act of supplying AI coding agents with relevant data and capabilities to improve the accuracy and relevance of their outputs. It also involves optimizing the breadth of information to guide efficient processing. Such context can include coding style, internal libraries, institutional knowledge, production data, and external data from platforms like Slack, Atlassian, Notion, or GitHub, among others.
“MCPs support context engineering because it creates a standard way for AI systems to connect to various business tools,” says Todd Olson, CEO of Pendo, a product experience platform. “The key benefit is that the agent determines what context it needs based on the question, then uses the appropriate MCP server to fetch that information in real time.”
With the rise in AI-assisted coding, MCP is becoming a doorway for real-time dynamic search and retrieval across various sources, playing an important role in context engineering efforts. As Joey Stout, solutions architect at Spacelift, an infrastructure orchestration platform, puts it, MCP is the “saving grace of vibe coding.”
How MCP boosts context engineering
Using MCP, agents can fetch structured data contextually relevant to the task at hand. According to Edgar Kussberg, group product manager at Sonar, MCP accelerates the knowledge-hunting engineers must routinely perform on a daily basis.
