Model Context Protocol (MCP) is emerging as a key enabler of context engineering for AI coding agents. By providing a standard interface for agents to dynamically fetch relevant data at runtime — from documentation, code repositories, CI systems, Sentry errors, SonarQube security reports, and more — MCP reduces hallucinations, optimizes token usage, and improves output accuracy. Industry experts highlight MCP's advantages over static RAG pipelines, its role in enterprise-scale agentic workflows, and its potential to become foundational infrastructure. Caveats include token bloat from large MCP server portfolios, security considerations around access controls, and the need for governed MCP registries. Growth data shows a 232% increase in MCP servers over six months, with read operations dominating, confirming their primary use for data retrieval.
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