Model Context Protocol (MCP) servers are rapidly becoming the backbone of a new era of collaborative AI, by delivering an integration layer that enables seamless communication and cooperation between diverse and specialized artificial intelligence agents.
According to Gartner, MCPs are expected to become a widely adopted standard for API gateway vendors. Specifically, 50% of Integration Platform as a Service (iPaaS) vendors will adopt MCPs by 2026. During the Build 2025 conference, Microsoft emphasized that the future of AI agents is directly linked to open standards and shared infrastructure, which will enable unique capabilities for customers. The company aims to provide extensive first-party support for MCP across its agent platforms and frameworks, including GitHub, Copilot Studio, Dynamics 365, Azure AI Foundry, Semantic Kernel and Windows 11.
This article will explain what the MCP is, how MCP servers operate, their significance and the advantages MCP integration brings when implemented in enterprises by an experienced team.
What is Model Context Protocol (MCP)?
Originally designed as a standardized method for individual AI models to interact with various tools and data sources, the MCP standard – introduced by Anthropic in November 2024 – has evolved into a fundamental technology for multi-agent systems. The MCP is an open standard that allows developers to create secure, two-way connections between their data sources and AI-powered tools. Sometimes called USB-C for AI tools, it enables plug-and-play combability, by making it possible to develop a tool that can be used by any agent. The architecture is simple: developers can either share their data through MCP servers or create AI applications (MCP clients) that connect to these servers. However, to truly build AI systems that deliver business value, enterprises first need an integration layer that lets AI safely tap into existing systems and data. MCP servers deliver just that.
MCP server as integration layers?
Lightweight programs or services that act as an adapter for a specific tool or data source, and MCP server exposes certain functionalities of that tool in a standardized manner. Instead of requiring the AI to understand the details of a specific API, such as Salesforce, or a SQL database, the MCP server informs the AI about the “tools” it offers – for example, looking up a customer by email or a query to retrieve today’s sales total. It works like a contract: the MCP server defines what it can do in a machine-readable format and how to call its functions. The AI model can read this contract and comprehend the available actions.
At its core, the MCP follows a client–server architecture. On one side is the MCP client (built into the AI application or agent), and on the other side are one or more MCP servers (each connecting to a specific system or resource). The AI-powered app – for example, an AI assistant like Claude or ChatGPT, or a smart (integrated development environment) IDE – acts as the MCP host that can connect to multiple servers in parallel. Each MCP server might interface with a different target: one could connect to a cloud service via its API, another to a local database, another to an on-premise legacy system. Crucially, all communication between the AI (host) and the servers follows the standardized MCP protocol, which uses structured messages to format requests and results consistently.
One significant feature of the MCP is that it changes the integration model. Instead of hard-coding an AI to use a specific API, the system informs the AI about its actions and how to perform them. The MCP server essentially communicates, “Here are the functions you can call and the data you can access, along with descriptions of each.” This allows the AI agent to discover these functions at runtime and invoke them as needed, even combining multiple tool calls to achieve a goal. In essence, MCP decouples the AI from being tied to any particular backend system. As long as a tool has an MCP server, any MCP-enabled AI can utilize it.
Why MCP matters for enterprises
By allowing companies to take advantage of AI solutions and accelerate their business, MCP servers will play an essential role in the enterprise context. Here’s what’s most crucial:
Breaking down silos with a unified standard: Enterprises often use a combination of legacy systems, modern cloud applications and proprietary databases. MCP simplifies this landscape by replacing numerous individual integrations with a single standard protocol. This enables AI systems to access data from all these sources in a consistent manner. As a result, redundant integration efforts are eliminated, and developers only need to create or adopt an MCP connector once. In this way, any AI agent can utilize it without reinventing the wheel for each new model or tool.
Making AI agents useful: By giving AI real hooks into business systems, MCP turns AI from a passive Q&A assistant into an active problem-solver. An AI agent with MCP can actually do things like, for example, retrieving current sales figures, cross-searching support tickets, initiating workflows – not just talk about them. This is the difference between an AI that’s a nifty demo and an AI that’s a true teammate that gets work done. Early adopters have shown AI agents performing multi-step tasks like reading code repositories or updating internal knowledge bases. Thanks to MCP, organizations are achieving real productivity gains.
Vendor-neutral and future-proof: MCP is being embraced by major AI players – Anthropic, OpenAI, Microsoft (Copilot Studio) and others – which means it’s on track to become a common language for AI integrations. Enterprises will not be locked into a single AI vendor’s ecosystem, as a connector designed for the Multi-Cloud Platform (MCP) can work with any compliant AI model. This flexibility allows organizations to switch models without disrupting their existing tool integrations. As the MCP ecosystem continues to mature, we are witnessing the emergence of marketplaces for MCP servers tailored to popular applications like GitHub, Notion and Databricks, which organizations can integrate with minimal effort.
Reduced maintenance and more resilience: Standardizing how AI connects to systems means less brittle code and fewer surprises when things change. MCP essentially decouples the AI from the underlying API changes – if a service updates its API, you only need to update its MCP server, not every AI integration that uses it. It’s also possible to work on versioning and contract evolution so that tools can update without breaking the AI’s expectations. This leads to more sustainable, scalable architectures.
There’s an additional crucial reason why the MCP plays such an important role for enterprises, and that’s security and access control – it’s worth focusing on in a separate section.
Security and access control in MCP integrations
Any system that connects AI models with sensitive company data must take security seriously. As Red Hat puts it, the MCP itself is a specification, not a magic security shield. However, it provides a framework that enterprises can implement securely.
MCP servers and their developers are encouraged to adhere to the principle of least privilege. In practice, this means that an MCP server should request only the minimum set of permissions necessary to perform its functions – nothing more. For example, if an AI agent only needs read access to a database to answer queries, the MCP integration should not ask for delete or admin rights.
The guiding rule is simple: the AI agent, through the MCP, operates under the user’s identity and permissions. For instance, if an employee requests an AI assistant to generate a report, the request can be executed via MCP using that employee’s credentials or a delegated access token. This approach ensures that the AI can only access data that the user is permitted to see. This user-based access control is essential in industries with strict data governance, as it prevents an AI solution from inadvertently accessing restricted data.
Moving forward, the community, in line with Red Hat’s publication, plans to implement more fine-grained security layers and permission schemas designed explicitly for interactions with AI tools. We may soon see the introduction of governance features for Managed Cloud Platforms (MCP), so administrators can control which tools an AI agent can utilize centrally and the types of data it can access.
Importantly, since MCP connectors are often open-source or auditable, enterprises can inspect and verify an MCP server’s actions when it processes data. This level of transparency fosters trust, as organizations do not need to regard AI as a black box that operates without oversight. Instead, there will be verifiable and controlled interfaces for every action the AI performs, which is essential for compliance and security auditing.
MCP severs integration – an AI-powered platform shift for enterprises
Aware of the importance of MCP servers, Software Mind developed a custom solution for enterprises that delivers an integration layer between your legacy systems and modern technologies for enterprises. The platform is composed of four main components designed to create a comprehensive multi-agent system:
- LLM <–> Cloud: This component leverages cloud-based language models to create an intelligent communication layer, enabling advanced interaction and data processing.
- On-prem: It offers the flexibility of on-premise deployment, allowing the system to be installed within a client’s existing IT environment to ensure data security is maintained.
- MCP Servers: This is the core of the platform, acting as a central hub that manages all communication between different systems and seamlessly integrates with existing infrastructure.
- Multi-Agent Framework: This component establishes an organized ecosystem where multiple AI agents can collaborate. It functions as a structured system to support and streamline various processes.
Our architecture seamlessly combines all layers – from foundational infrastructure and integration with existing systems, up to advanced multi-agent orchestration. This holistic approach enables the creation of secure and highly scalable, enterprise-ready AI solutions that can leverage databases, APIs, storage, and legacy systems as fully integrated tools within your organization. With centralized governance, robust cost control, and comprehensive monitoring built in, organizations gain full visibility and control over every aspect of their AI environment – ensuring compliance, efficiency, and operational excellence at scale.
MCP servers – the next stage of AI evolution
MCP servers and the Model Context Protocol represent a significant leap in integrating AI into enterprise fabric. In the past, organizations struggled to make AI initiatives more than just flashy demos, because connecting AI to real business processes was slow and costly. Now, by building a dedicated integration layer with MCP, companies can deploy AI that is actually useful, from day one.
After the heightened popularity of AI following the generative AI boom, the next phase will focus on how well AI integrates into our existing systems and workflows. MCP servers serve as the bridge between today’s AI and yesterday’s infrastructure.
If you want to leverage MCP servers, modernize your existing tech stack and truly take advantage of AI, contact one of our experts using this form.
About the authorDamian Mazurek
Chief Innovation Officer
A certified cloud architect and AI expert with over 15 years’ experience in the software industry, Damian has spent the last several years as a cloud and AI consultant. In his current role he oversees the technology strategy and operations, while working with clients to design and implement scalable and effective cloud solutions and AI tools. Damian’s cloud, data and machine learning expertise has enabled him to help numerous organizations leverage these technologies to improve operations and drive business growth.
