As organizations race to deploy AI solutions, Model Context Protocol (MCP) has emerged as the universal connector, successfully eliminating integration complexity. One of the biggest challenges in scaling AI has been the need to build and maintain countless custom integrations, an approach that quickly becomes costly and unsustainable as use cases expand. MCP is designed to move beyond the limitations of model-specific approaches, creating a framework that any language model can adapt. It’s a new open standard that makes it easier for AI models to connect with external data, API and services, enabling scalable, secure and interoperable AI agent ecosystems.
The MCP is growing in popularity fast. In March 2025, OpenAI officially adopted the MCP on its platform, followed by Microsoft investing in the MCP to enhance AI integration across its ecosystem. The reason? MCP standardizes how AI agents connect with other data and tools and systems, regardless of their location. Let’s break down why enterprises struggle to create and scale AI agents, how MCP simplifies that and why MCP should be on every IT leader’s roadmap.
The Rising Challenges of Enterprise AI Integration
Why custom connectors don’t scale for AI agents
Enterprise IT leaders face limitations in integrating AI agents with critical systems. Historically, this required custom-built connectors for every tool, database, and service it needs to access, which require continuous maintenance and developers’ bandwidth. While it’s achievable at a smaller scale, this approach fails to deliver an enterprise-level solution, where dozens or even hundreds of systems would need to interoperate.
The N×M problem: exponential integration complexity
In a multi-system enterprise, where every new system must interact with another, creating custom connectors leads to an NxM integration problem. Essentially, this means that every one of N tools must be integrated with every one of M models or agents, causing unmanageable complexity as the ecosystem grows. To break this down further, this could mean 400 customer connectors for 20 systems. Adding AI agents on top that require real-time access across applications magnifies the complexity manifold.
Why do IT leaders need a standardized approach?
Much like TCP/IP once standardized how systems communicated across the internet, MCP is emerging as a common protocol layer for enterprise AI. By providing a consistent way for AI agents to connect with systems and services, it simplifies AI agent development, reduces integration costs, and lays down the groundwork for scalable multi-agent AI ecosystems. Without it, AI adoption risks being fragmented, complex, expensive, and insecure.
How The Model Context Protocol Works
MCP architecture and core components
MCP was released by Anthropic last November, described as “a new standard for connecting AI assistants to the systems where data lives, including content repositories, business tools and development environments.” [1]
It works on a simple client-server architecture comprising three core components, allowing interactions across data sources, AI models, or services: MCP Servers, MCP Hosts, and MCP Clients. Let’s understand them in more detail:
MCP Host: The AI application that coordinates and manages one or multiple MCP clients.
MCP Client: A component that serves as the intelligent interface integrated within the AI applications. It’s responsible for discovering server capabilities and facilitating data exchange with large language models. The MCP clients automatically identify available resources and maintain session-based interactions, enabling multi-step, complex workflows essential for enterprises.
MCP Server: A program that provides context to MCP clients. It acts as a gateway to enterprise resources, exposing tools, data sources, and prompt templates through standardized interfaces. MCP servers are designed to support dynamic discovery that allows AI agents to inquire about their capabilities, such as “What can you do?” and receive a comprehensive description in real-time.
This architecture not only simplifies but redefines how AI agents interact with enterprise systems, even at a larger scale.
Figure 1: Multi-Agent AI Enterprise Architecture: Interconnected AI Agents, Software Applications, and Compliance Frameworks
Communication protocols: JSON-RPC, HTTP, and beyond
MCP primarily uses JSON-RPC 2.0 as its underlying Remote Procedure Call (RPC) protocol. Clients and servers exchange requests and responses using this lightweight and efficient standard. While JSON-RPC is commonly implemented over HTTP in other contexts, the MCP reference implementations runs over stdio as it’s transport-agnostic. It is designed to be extensible, with potential for WebSockets and other transports in future iterations to enable real-time, autonomous multi-agent systems. [2]
One of the primary benefits of MCP is that it enables AI agents to utilize enterprise services without the need for customized integrations or hard-coded logic. Serving as a standardized bridge between generative AI applications and cloud services, MCP empowers agents to convert natural language queries into structured actions, eliminating the need for customized connectors.
Why Are Enterprises Rapidly Adopting MCP?
MCP has become the accepted standard for integrating AI, indicating a remarkable consensus in the industry around a unified strategy for developing AI agents.
From Anthropic, OpenAI, Google, Microsoft, IBM, and Amazon, major industry giants have already recognized the need. [3]
According to Gartner’s 2025 Software Engineering Survey, by 2026, 75% of API gateway vendors and 50% of iPaaS vendors will have MCP features. [4] The report also indicates optimistic forecasts for the adoption of MCP, highlighting its significant influence on enterprise technology stacks.
Early Adopters and real-world implementation
Even though it was only unveiled in late 2024, MCP has garnered enthusiastic early support from both businesses and open-source initiatives. These initial applications offer a preview of MCP’s functionality in real-world scenarios.
Microsoft Azure
Microsoft Azure announced in May 2025 that it has incorporated MCP directly into the Azure AI Agent Service, allowing developers to:
- Easily access real-time web data through Bing Search
- Internal, private data via Azure AI Search.
This integration showcases MCP’s readiness for enterprise use, with Microsoft layering OAuth 2.1 authentication, robust security measures, and scalable cloud deployment strategies on top of the protocol. [5]
IBM
IBM has recently created a wide range of MCP tools, such as the Context Forge gateway, integrations with watsonx.ai, and document library retrieval servers that link AI agents to enterprise data resources. Their strategy focuses on ready-to-use deployments featuring advanced identity management, comprehensive audit logging, and security frameworks that support multiple tenants. [6]
Block & Apollo
Anthorpic’s announcement specifically mentioned Block (formerly Square) and Apollo as early adopters integrating MCP into their systems. [7] These enterprises are leveraging MCP to build agentic AI systems that automate complex business processes while maintaining security and governance controls.
“At Block, open source is more than a development model—it’s the foundation of our work and a commitment to creating technology that drives meaningful change and serves as a public good for all.”
-Dhanji R. Prasanna, Chief Technology Officer at Block.
The ecosystem extends beyond major vendors to include specialized providers like Stripe, Cloudflare, JetBrains, and Replit, each contributing MCP servers that deliver industry-specific capabilities to AI agents. This expanding marketplace of MCP-compatible services fosters network effects that boost both adoption and innovation throughout the ecosystem.
How MCP Strengthens AI Agent Development
For years, the process of AI agent development has been hindered by disjointed integrations. Each time an agent required access to a different system, developers had to create custom connectors, an approach that lacked scalability and soon led to the N×M integration challenge. MCP removes this obstacle by offering a standardized, reusable framework that simplifies integration to the level of connecting through a universal port.
The MCP simplifies AI agent development in four critical ways:
- Standardized Integrations: A unified protocol replacing one-off connectors, reducing complexity.
- Flexibility: MCP is model/platform agnostic. As long as SLM or LLM supports MCP, integrations work.
- Capable Agents: Agents shift from rigid scripts to adaptive systems that can plan, discover, and execute workflows autonomously
- Reliability: Due to the structured request and response format mandated by MCP, it naturally promotes improved error management and communication. MCP servers are designed to identify errors and return significant messages that the AI can comprehend.
Figure 2: Advantages of building AI agents using MCP
The Business Value of MCP for AI Agents
MCP delivers measurable business outcomes across multiple aspects, directly impacting enterprise AI initiatives.
Dramatic Reduction in Integration Costs
One of MCP’s most significant impacts is on the integration costs. Companies using MCP save 25% of the time to build AI systems with many models. [8] The efficiency arises from MCP’s integration model, which recycles resources and converts quadratic complexity expansion into linear scalability as the AI agent deployments increase.
Scaling AI agent deployments with less friction
Using MCP, introducing a new AI agent does not entail duplicating integration efforts. AI agents connect with existing MCP servers and quickly identify accessible enterprise resources, significantly minimizing obstacles when scaling.
Enterprise-grade security with OAuth 2.1 and RBAC
The standardized authentication framework of the protocol is built on OAuth 2.1 with PKCE support, ensuring robust security controls for enterprises while providing a seamless access experience across all interactions with AI agents. This approach minimizes security vulnerabilities commonly found in ad-hoc integration methods, while also facilitating thorough audit trails and governance measures.
Dynamic adaptability for evolving business needs
MCP’s dynamic discovery capabilities ensure that AI agents automatically adapt when systems or processes are changed, minimizing disruption and upkeep.
OneReach.ai’s Generative Studio X (GSX) enhances the advantages of MCP with a no-code interface for integrating external MCP servers. Organizations can securely connect AI agents to thousands of MCP-compatible services with instant compliance verification, OAuth 2.1 authorization, and centralized governance. This significantly lowers integration expenses while maintaining enterprise-grade security and scalability.
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Download the Strategy GuideReal World MCP Use Cases
Enterprise applications of MCP showcase its adaptability across a wide range of industries and use cases.
- Financial services: powering advanced analytics
Financial firms are using MCP-enabled AI agents for real-time risk assessment, fraud detection, and compliance monitoring. Quantium, a global leader in data analytics and AI consulting, uses MCP-enabled AI agents to power company-wide adoption across its 1200+ team members, enabling sophisticated analytics workflows that span multiple data sources and analytical tools. [9]
- Publishing: integrating research and knowledge access
Wiley has adopted MCP to enable seamless integration between authoritative, peer-reviewed content and AI tools across multiple platforms, establishing standards for proper attribution and citation while providing enhanced access to scientific research. [10]
“The future of research lies in ensuring that high-quality, peer-reviewed content remains central to AI-powered discovery.”
Josh Jarrett, Senior Vice President of AI Growth at Wiley.
- Software development: context-aware coding agents
Several platforms like GitHub Copilot, Zed, Sourcegraph, and Codeium now use MCP to provide AI agents with real-time access to project context, enabling more intelligent code suggestions and automated development workflows. [11]
- Customer support: AI agents for faster resolution
Many organizations have implemented MCP-enabled AI agents in their customer support processes to automatically access accounts, check billing records, verify payment, and update subscription information across multiple systems. This allows faster resolution with minimal human intervention. In fact, Gartner predicts that by 2028, 33% of enterprise software will include agentic RAG capabilities, up from less than 1% now. [12]
MCP Implementation: Best Practices for IT Leaders
Start with high-value, multi-system use cases
Identify AI agent applications that require integration with numerous enterprise systems and can demonstrate clear ROI. Begin with prototype implementations that expose internal tools and data sources through MCP servers, all while maintaining stringent security measures.
Build a robust security and governance framework
Deploy comprehensive security measures, including OAuth 2.1 authentication, role-based access controls, version pinning to prevent unauthorized updates, and trust domain isolation. Treat all tool logic and MCP servers as potentially untrusted and implement private registries for curated server deployments.
Design for scalability and protocol evolution
Allocate resources for tracking MCP standard evolution and maintaining compatibility with emerging features. The protocol continues to evolve rapidly; enterprises should architect for modularity and extensibility, allowing adoption of new transports and standards without re-architecting.
To speed up implementation, platforms such as OneReach.ai Generative Studio X (GSX) offer specialized tools that simplify the complexities of MCP integration. The GSX Platform enables IT teams to quickly add, manage, and validate MCP server connections within minutes, guaranteeing reliable agent performance and minimizing delays of 4–6 months associated with adapting legacy systems.
Strategic Recommendations for IT Leaders
According to industry evaluations and early adoption trends, IT executives ought to think about these strategic methods for implementing MCP:
- Adopt a Portfolio Approach: Instead of carrying out separate pilot initiatives, create a comprehensive AI agent strategy that uses MCP as the core integration layer. This method enhances the cumulative advantages of uniform connectivity while building organizational capabilities in AI agent development.
- Invest in Security and Governance: Prioritize security implementation with a dedicated focus on authentication frameworks, access controls, and audit capabilities. Engage the legal teams early to address regulatory requirements and risk management protocols.
- Build Internal Capabilities: Develop expertise in MCP server development and client integration, while creating hubs of excellence for AI agent development. This investment allows organizations to tailor solutions while preserving autonomy from vendor lock-in.
- Foster Ecosystem Participation: Connect with the wider MCP community by contributing to open-source projects, forming industry collaborations, and participating in standards development initiatives. Being actively involved enhances learning and shapes the evolution of protocols to align with enterprise needs.
- Plan for Scalability: Develop MCP implementations that meet enterprise-level demands, such as multi-region deployments, high availability architectures, and thorough monitoring capabilities. Utilize cloud-native services like Azure Functions, AWS ECS, and Google Cloud Run to achieve scalable deployments of MCP servers.
MCP as the Foundation for AI-Driven Enterprise Transformation
The Model Context Protocol (MCP) is a major shift toward standardized, scalable, and secure integration of AI, which is paramount for enterprise AI transformation. For IT executives, MCP offers the foundational support needed to move from small-scale pilot programs to extensive AI implementations that generate tangible outcomes.
Organizations that adopt MCP early can experience reduced development expenses, quicker deployment times, and enhanced operational adaptability. But successful implementation is key.
To successfully adopt MCP, it is essential to have a clear strategic vision, technical skills, and a focus on security and governance. With enterprises increasingly leaning towards AI, embracing MCP could accelerate adoption while ensuring security and reliability.
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Book a DemoRelated Questions about implementing Model Context Protocol?
1: What is the MCP standard for AI agents?
The MCP standard provides a unified framework for AI agents to dynamically discover and interact with enterprise resources, reducing integration complexity and enabling scalable and secure AI ecosystems.
2: Why should you use MCP instead of a REST API?
MCP offers dynamic discovery and interaction capabilities that REST APIs lack. It enables AI agents to query systems in real-time, supporting more autonomous workflows.
3: How is MCP different from LangChain?
MCP is a protocol for integrating AI agents with enterprise systems, while LangChain is a framework for building LLM-based applications. MCP standardizes interactions; LangChain accelerates application development.
4: Is MCP just an API wrapper?
No. MCP is a full-fledged protocol that reduces integration debt, ensures security, and supports scalable multi-agent ecosystems.
