Kumar Chivukula, co-founder and CEO of Codeglide.ai (a subsidiary of Upsera), explains why the rise of the Model Context Protocol (MCP) is reshaping how enterprises connect APIs to large language models. For years, APIs have served as the backbone of data access, but they were never designed with AI in mind. They lack memory, context, and intent awareness—forcing developers to bolt on brittle glue code every time models change.

Anthropic’s introduction of MCP earlier this year marked a turning point, offering a standardized way to make APIs context-aware and AI-ready. But as Chivukula points out, adopting MCP isn’t just about creating a one-off server. Enterprises run tens of thousands of APIs—many undocumented, outdated, or internally facing—and keeping pace with rapid model changes demands a lifecycle approach. That’s where CodeGlide positions itself: a continuous MCP server platform that automates creation, updates and security scanning at scale.

The comparison to cloud migration is apt. Just as lift-and-shift approaches left organizations with bloated costs, treating MCP as a quick wrapper on existing APIs won’t cut it. Instead, enterprises need a sustainable framework that refactors APIs for AI interaction, enforces security, and manages change continuously. Chivukula highlights integration with GitHub’s vast ecosystem—home to tens of millions of API repositories—as a critical lever to reach both developers and enterprises.

With more than a billion APIs in existence and an emerging AI economy valued in the tens of billions, MCP adoption isn’t optional—it’s inevitable. The question is how organizations will navigate the transition without drowning in complexity. Platforms like CodeGlide hint at one possible path forward.