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Model Context Protocol (MCP) Is Changing How AI Integrates and Why It Matters

Integrating AI into enterprise systems is still one of the most complex and time-consuming challenges in modern IT. For most organizations, enabling large language models to interact with existing tools, such as internal databases, collaboration platforms, or customer relationship systems, often means building custom APIs for each connection. This approach drains time, adds cost, and slows down innovation.

The concern is not limited to developers. In ‘The Impact of Technology in 2024 and Beyond: an IEEE Global Study’, 47 percent of global technology leaders listed difficulty integrating AI into existing workflows among their top three challenges when adopting generative AI[1]. That reflects an apparent demand for more scalable and sustainable integration methods.

This is where the model context protocol, or MCP, is gaining momentum. This article explores MCP, how it works under the hood, who’s building around it, and why it matters for enterprise AI strategy. We’ll also look at the broader impact of standardized AI integrations and what this means for service providers and global businesses.

What is MCP, and how does it work?

The model context protocol is a standardized AI protocol introduced by Anthropic on November 25, 2024, to streamline how large language models (LLMs) like Claude interact with tools, services, and enterprise systems[2].

It was developed as part of the Claude ecosystem. It is now gaining broader recognition across the AI community for enabling scalable, plug-and-play integrations between models and external applications.

Earlier this month, I had the opportunity to participate in a panel discussion about the role of AI in product development. One of the key topics we discussed was the importance of the model context protocol as a foundational enabler of agentic, autonomous systems.

From my perspective, MCP is key to advancing agentic AI, enabling systems that don’t just respond but take the initiative, adapt to real-time enterprise data, and collaborate across tools without manual intervention. This approach opens the door for faster decision-making, streamlined workflows, and AI that operates more intelligently within complex environments.

At its core, MCP simplifies how AI models communicate with external services. Rather than building a custom integration for every platform or API, MCP provides a reusable framework that models can use to send requests, receive data, and trigger real-time actions, without knowing the specifics of each system. This reduces development cycles and improves interoperability across enterprise environments.

This architecture follows a clean three-part model:

  • Host: The AI application or interface that initiates a request, such as generating a summary or retrieving structured data.
  • Client: Acts as the translator. It routes the model’s request to the appropriate destination and manages system exchange.
  • Server: The execution layer. It connects to external tools like cloud storage, internal databases, or other remote services, fetches or processes the data, and returns a structured response to the model via the client.

MCP creates the foundation for scalable AI integration by operating over common standards like JSON-RPC, reducing the need for plugins, custom APIs, or middleware. This allows developers to focus on building intelligent applications while models handle complex multi-system workflows more autonomously.

Who’s building around MCP, and why does it signal broader industry alignment?

The growing adoption of MCP isn’t limited to its origin at Anthropic. A handful of leading organizations are now contributing to or adopting the standard, helping it evolve from a protocol to a platform-level enabler for enterprise AI.

 
Google

Google

Google has officially adopted MCP for its Gemini and SDKs[4]. This integration signals Google’s endorsement of MCP as the emerging open standard for tool-model connectivity. For enterprises building multi-agent workflows on Gemini, this offers a more unified and future-proof integration path.

Microsoft

Microsoft

Microsoft has introduced MCP support in Copilot Studio, enabling developers to streamline how AI agents interact with apps, APIs, and datasets[5]. Its developer blogs now feature detailed guides for implementing MCP using Azure AI tools, indicating a serious ecosystem investment.

Open-source contributors via GitHub

Open-source contributors via GitHub

The model context protocol GitHub repository is home to a growing set of SDKs, reference servers, and integration templates[6]. This open development model invites cross-platform experimentation, driving adoption in smaller teams and startups, and accelerating MCP’s reach into practical, community-driven AI workflows.

Why does shifting from traditional integrations to MCP matter?

Most traditional AI integrations are built in silos and don’t scale well. MCP offers a standardized, reusable approach that simplifies how models connect and operate across systems.

Traditional integration approachModel context protocol
Integrations are custom-built for each tool or system, increasing time and complexity.MCP uses a reusable, standardized protocol to connect AI models with any tool or data source.
Model behavior varies across environments due to inconsistent setups, making results hard to reproduce.It centralizes datasets, configurations, and context for consistent, reproducible performance.
Teams often build redundant solutions due to a lack of interoperability and standards.It enables inter-team and cross-org reuse through a shared, open standard.
Hard to align with open workflows due to mismatched metadata and configuration It follows a consistent structure that supports streamlined integration across AI workflows.
Limited flexibility to scale or adapt models across functions or deployments.It is designed to scale with minimal rework and is ideal for evolving enterprise AI strategies.

Why is MCP becoming essential for developers and enterprise leaders alike?

As AI adoption accelerates, developers and decision-makers face rising pressure to deliver speed, flexibility, and seamless integration at scale.

Yet most AI projects still hit the same roadblocks: manual integrations, lengthy deployment cycles, and tool-specific customization that doesn’t scale across environments.

MCP addresses these challenges by enabling simplified AI integrations, tighter security, and scalable AI deployment without adding unnecessary engineering complexity.

Development cycles drop from hours to minutes

Development cycles drop from hours to minutes

MCP streamlines integration by abstracting away repetitive, tool-specific logic. Tasks that once required full-day engineering sprints, like setting up secure infrastructure or managing permissions, can now be handled in minutes.

This gives developers more time to focus on innovation instead of maintenance. For enterprises, it reduces deployment timelines and accelerates time-to-market, freeing up resources for higher-priority initiatives.

Development cycles drop from hours to minutes

MCP streamlines integration by abstracting away repetitive, tool-specific logic. Tasks that once required full-day engineering sprints, like setting up secure infrastructure or managing permissions, can now be handled in minutes.

This gives developers more time to focus on innovation instead of maintenance. For enterprises, it reduces deployment timelines and accelerates time-to-market, freeing up resources for higher-priority initiatives.

Security improves through local data control

Security improves through local data control

Traditional AI integrations often rely on API bridges and cloud endpoints, widening the surface area for breaches. A recent survey shows that 60 percent of organizations have experienced at least one API-related breach[7].

MCP reduces exposure by enabling localized data interactions and more governed access patterns. However, its specifications may not fully align with specific enterprise security frameworks, such as OAuth-based protocols in platforms like AWS. These gaps must be carefully evaluated before integration to ensure security and compliance are not compromised.

Security improves through local data control

Traditional AI integrations often rely on API bridges and cloud endpoints, widening the surface area for breaches. A recent survey shows that 60 percent of organizations have experienced at least one API-related breach[7].

MCP reduces exposure by enabling localized data interactions and more governed access patterns. However, its specifications may not fully align with specific enterprise security frameworks, such as OAuth-based protocols in platforms like AWS. These gaps must be carefully evaluated before integration to ensure security and compliance are not compromised.

Domain-level expertise without the overhead

Domain-level expertise without the overhead

MCP enables AI models to access and apply specialized domain knowledge stored across enterprise systems, without retraining or manual configuration. Developers can query technical systems and receive context-specific responses that align with the best practices and internal standards.

This removes the need to consult external experts or spend hours reading technical documentation. For businesses, it reduces dependencies and enables teams to move faster and more confidently.

Domain-level expertise without the overhead

MCP enables AI models to access and apply specialized domain knowledge stored across enterprise systems, without retraining or manual configuration. Developers can query technical systems and receive context-specific responses that align with the best practices and internal standards.

This removes the need to consult external experts or spend hours reading technical documentation. For businesses, it reduces dependencies and enables teams to move faster and more confidently.

Guided implementation of best practices

Guided implementation of best practices

With MCP, secure defaults and best practices aren’t enforced automatically; they’re recommended. Whether activating encryption, enforcing access controls, or enabling audit logging, MCP provides guidelines to help developers implement guardrails as per their environment’s needs.

This is especially valuable in regulated industries where consistency and compliance aren’t negotiable. Developers benefit from structured direction, and enterprises gain confidence in how AI connects with sensitive systems.

Guided implementation of best practices

With MCP, secure defaults and best practices aren’t enforced automatically; they’re recommended. Whether activating encryption, enforcing access controls, or enabling audit logging, MCP provides guidelines to help developers implement guardrails as per their environment’s needs.

This is especially valuable in regulated industries where consistency and compliance aren’t negotiable. Developers benefit from structured direction, and enterprises gain confidence in how AI connects with sensitive systems.

Flexibility and scalability across use cases

Flexibility and scalability across use cases

MCP isn’t locked to any single model, tool, or platform. Its standardized design allows it to scale with business needs across departments, cloud environments, and domains without requiring complete rewrites each time a new system is introduced.

This adaptability makes it easier for IT leaders to future-proof their AI investments while keeping operational complexity low. It also allows developers to build once and scale broadly without fragmentation.

Flexibility and scalability across use cases

MCP isn’t locked to any single model, tool, or platform. Its standardized design allows it to scale with business needs across departments, cloud environments, and domains without requiring complete rewrites each time a new system is introduced.

This adaptability makes it easier for IT leaders to future-proof their AI investments while keeping operational complexity low. It also allows developers to build once and scale broadly without fragmentation.

MCP is laying the groundwork for the next era of intelligent AI systems

As AI continues to scale across enterprises, the real challenge lies in how models can connect, act, and adapt within business environments. MCP addresses this challenge at the foundation, making AI solutions faster to deploy and more aligned with enterprise needs from day one.

MCP’s plug-and-play structure enables models to go beyond task execution and become operational participants. This could look like an AI assistant coordinating project calendars across teams or a healthcare model securely accessing patient data while preserving privacy.

It can support manufacturing analytics without manual data pulls or automate compliance checks in finance, all without rebuilding integrations for each use case.

These are not futuristic visions. They are practical, scalable use cases that MCP already enables through its open, standardized interface.

As we look ahead, the impact of MCP will be operational. It brings AI into systems, securely and efficiently, while giving organizations the flexibility to evolve without rebuilding from scratch.

If you’d like to explore how MCP could fit into your AI strategy, contact our team to see how we can help.

Sources

  1. IEEE, Using AI Isn’t As Easy As It Seems, Raul Colcher, IEEE Life Senior Member, October 26, 2023: https://transmitter.ieee.org/using-ai-isnt-as-easy-as-it-seems/
  2. Anthropic, Introducing the Model Context Protocol, November 25, 2024: https://www.anthropic.com/news/model-context-protocol
  3. Gulf Insight 360, Google Embraces Anthropic’s MCP to Enhance AI Data Integration 2025, Newsdesk, April 11, 2025: https://gulfinsight360.com/google-embraces-anthropics-mcp/
  4. TechCrunch, Google to embrace Anthropic’s standard for connecting AI models to data, Kyle Wiggers, AI Editor, April 9, 2025: https://techcrunch.com/2025/04/09/google-says-itll-embrace-anthropics-standard-for-connecting-ai-models-to-data/
  5. Microsoft, Introducing Model Context Protocol (MCP) in Copilot Studio: Simplified Integration with AI Apps and Agents, Zankar Desai, April 9, 2025: https://www.microsoft.com/en-us/microsoft-copilot/blog/copilot-studio/introducing-model-context-protocol-mcp-in-copilot-studio-simplified-integration-with-ai-apps-and-agents/
  6. GitHub, Model Context Protocol: https://github.com/modelcontextprotocol
  7. Security Magazine, 60% of organizations faced at least one API related breach, Security Staff, September 8, 2023: https://www.securitymagazine.com/articles/99883-60-of-organizations-faced-at-least-one-api-related-breach

About the author

Archana Joshi

Archana Joshi
Head Strategy and AI Services, LTIMindtree

Archana Joshi has over 24 years of experience in the IT services industry, specializing in artificial intelligence, particularly generative AI, as well as agile and DevOps methodologies, and green software practices. At LTIMindtree, she leads growth strategies and market positioning for the Enterprise AI service line and the Banking and Financial Services business unit. Archana has worked with Fortune 100 clients across various geographies and often speaks at major industry forums and events.


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