How MCP is transforming AI integration for enterprises

As Artificial Intelligence (AI) adoption accelerates across industries, there is an urgent need for context-specific information. This has led to the introduction of Model Context Protocol (MCP), which is a new framework for how large language models (LLMs) interact with external knowledge in a secure and dynamic way.

AI startup and the maker of Claude LLM, Anthropic developed MCP in November 2024 as a new standard for ‘connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments’. It aims at helping AI models to produce better and more relevant responses.

Rather than encoding all possible information into a model upfront (which is costly, time-consuming, and quickly outdated), MCP enables developers and businesses to pass in "context packs" that give the model temporary access to relevant data, rules, documents, or user preferences during inference.

Before MCP, developers were requireed to build custom integrations between LLMs and external tool. This resulted in high development and maintenance costs.

Enterprise adoption of MCP

In the coming months, MCP is expected to emerge as a core layer of enterprise AI architecture for more adaptable AI applications.

Major players in the AI ecosystem, including OpenAI, Anthropic, and enterprise AI platforms, are beginning to build support for protocols like MCP. While still early, the standardisation of context handling could become fundamental to AI systems as APIs are to traditional software.

Last week, at the 2025 developer conferences, both Microsoft and Google unveiled significant advancements in AI integration, emphasising the adoption of MCP to enhance interoperability and functionality across their platforms.

At Build 2025, Microsoft announced native MCP support in Windows 11, enabling secure, agentic AI across its ecosystem, including GitHub, Azure, and Dynamics 365. On the other hand, Google said that Gemini SDKs (software development kits) will now support MCP, making it easier for developers to use open-source tools and build smarter AI apps.

Closer home, this month, brokerage firm Zerodha introduced Kite MCP, enabling users to connect their trading accounts with AI assistants like Claude, Cursor, and Windsurf at no additional cost. This integration transforms AI assistant into a personalised financial advisor, which can access real-time market data, analysing portfolio, and providing insights in multiple languages.

Challenges with MCP

Despite its promise, MCP faces challenges. Data privacy and security top the list, as passing sensitive context to AI models risks exposing confidential information if not properly managed. Enterprises need transparency over what data is shared and how it’s used to ensure accountability and build trust.

Additionally, MCP is still an emerging standard. Diverse implementations across platforms could cause compatibility issues, making widespread adoption and clear standardisation important for better interoperability.