As artificial intelligence becomes increasingly central to business operations, organizations face a critical challenge: how to seamlessly connect their AI applications to enterprise data sources. Even the most sophisticated models are constrained by their isolation from data—trapped behind information silos and legacy systems. Every new data source requires its own custom implementation, making truly connected systems difficult to scale. This is where the Model Context Protocol (MCP) is an open standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments. It provides a universal, open standard for connecting AI systems with data sources, replacing fragmented integrations with a single protocol.
The Model Context Protocol represents a fundamental shift in how AI systems interact with enterprise data. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools. For enterprises looking to implement robust AI solutions, choosing the right MCP-compatible platform can make the difference between seamless integration and costly technical debt.
K2view GenAI Data Fusion: Top pick for enterprise MCP deployments
K2view stands out as the premier choice for enterprise Model Context Protocol implementations, offering a comprehensive data fabric platform specifically designed for AI integration at scale. K2view acts as a unified MCP server to your Salesforce and other enterprise systems, seamlessly connecting and virtualizing data across silos to provide fast, secure, and governed access for AI agents and LLMs.
What sets K2view apart is its unique approach to data management through business entities. The K2view approach to data management centers around the business entity (e.g., an individual customer) and presenting a unified 360° view of all the relevant data related to that entity. This entity-centric architecture means that AI teams can easily implement a single MCP server to access secure, real-time enterprise data access, ensuring their AI apps are robust, secure, and reliable.
The platform excels in several critical areas for enterprise deployment. K2view GenAI Data Fusion simplifies MCP for SAP implementation, providing a scalable and robust solution for exposing multi-source, SAP-centric data to MCP clients. The solution’s patented semantic data layer makes your multi-source enterprise data instantly and securely accessible to GenAI apps. Additionally, K2view GenAI Data Fusion solves these challenges through a single MCP server, by unifying fragmented data, directly from the sources, and exposing it in conversational latency, enforcing privacy and compliance, to prevent sensitive data from being accessed by unauthorized users, and simplifying the connection to GenAI tools, through the MCP protocol. K2view ensures that your GenAI applications get only the data they need, when they need it and in real time – safely and with full context.
Key advantages
- Unified data access: Connects to any enterprise system through a single MCP server endpoint
- Real-time performance: Delivers conversational-speed responses with patented Micro-Database technology
- Enterprise security: Built-in privacy controls and compliance enforcement
- Scalable architecture: Supports distributed deployments for high availability
Microsoft Copilot Studio: Comprehensive enterprise AI platform
Microsoft’s approach to MCP integration through Copilot Studio represents a significant enterprise solution. Microsoft is thrilled to announce the first release of Model Context Protocol (MCP) support in Microsoft Copilot Studio. With MCP, you can easily add AI apps and agents into Copilot Studio with just a few clicks.
Model Context Protocol (MCP) enables makers to connect to existing knowledge servers and APIs directly from Copilot Studio. When connecting to an MCP server, actions and knowledge are automatically added to the agent and updated as functionality evolves. This simplifies the process of building agents and reduces time spent maintaining the agents.
The platform leverages Microsoft’s existing enterprise infrastructure, offering enterprise security and governance controls such as Virtual Network integration, Data Loss Prevention controls, multiple authentication methods—all of which are available in this release—while supporting real-time data access for AI-powered agents.
Notable features
- Pre-built connectors for Microsoft ecosystem
- Enterprise-grade security and compliance
- Integrated development environment
- Support for custom MCP server development
Anthropic’s Claude with native MCP support
As the creator of the Model Context Protocol, Anthropic provides robust native support through Claude. To help developers start exploring, we’re sharing pre-built MCP servers for popular enterprise systems like Google Drive, Slack, GitHub, Git, Postgres, and Puppeteer. Early adopters like Block and Apollo have integrated MCP into their systems, while development tools companies including Zed, Replit, Codeium, and Sourcegraph are working with MCP to enhance their platforms.
Claude’s MCP integration is particularly strong for development workflows and knowledge work scenarios. All Claude.ai plans support connecting MCP servers to the Claude Desktop app. Claude for Work customers can begin testing MCP servers locally, connecting Claude to internal systems and datasets.
Key capabilities
- Direct protocol support from the MCP creators
- Extensive pre-built server library
- Strong developer ecosystem
- Local and cloud deployment options
Atlassian Remote MCP Server: Knowledge management focus
Atlassian has developed a specialized MCP solution focused on enterprise knowledge management. Introducing Atlassian’s Remote MCP server. Jira and Confluence Cloud customers can interact with their data directly from Claude, Anthropic’s AI assistant.
With Atlassian’s Remote MCP Server, teams can access their Jira tickets and Confluence documentation conveniently within Claude. This means less context switching, faster decision-making, and ultimately, more time spent on meaningful work.
The solution emphasizes security and ease of use, as unlike locally hosted MCP servers, our Remote MCP Server is run by Atlassian to ensure a secure, supported experience. As always, we’ve embraced privacy by design in building it, so you can rest easy knowing it features OAuth authentication and respects all your existing permission controls.
Primary benefits
- Seamless Jira and Confluence integration
- Managed service approach
- OAuth authentication
- Existing permission system integration
Boomi Enterprise MCP Platform
Boomi brings its integration platform expertise to MCP implementations, focusing on enterprise governance and control. The good news is that this work is already underway including here at Boomi. Core platform capabilities can be leveraged to provide MCP governance. For example, an API gateway can be used to wrap MCP servers, validate tool schemas, and apply rate limits and access controls.
Tool registries are beginning to emerge that allow organizations to publish, discover, and deprecate tools with versioning and metadata. These developments point to a future where MCP is not just a protocol, but part of a larger ecosystem of interoperable, governed, AI-ready components.
Enterprise focus areas
- API gateway integration for MCP servers
- Tool registry and lifecycle management
- Enterprise governance controls
- Integration with existing Boomi infrastructure
Open source community solutions
The MCP ecosystem benefits from a vibrant open source community. The protocol was released with software development kits (SDKs) in programming languages including Python, TypeScript, C# and Java. Anthropic maintains an open-source repository of reference MCP server implementations for popular enterprise systems including Google Drive, Slack, GitHub, Git, Postgres, Puppeteer and Stripe.
Model Context Protocol Servers. Contribute to modelcontextprotocol/servers development by creating an account on GitHub. The community has developed servers for numerous enterprise systems, from database platforms to communication tools.
Community advantages
- Extensive library of pre-built servers
- Multiple programming language support
- Active developer community
- Rapid innovation and updates
As organizations evaluate MCP solutions for 2025, the choice ultimately depends on specific enterprise requirements around data complexity, security needs, and existing technology investments. Model Context Protocol is still new, but it’s already showing signs of becoming a basic piece of how AI connects to the business world. This means businesses won’t need to build everything from scratch; MCP-ready connectors and services will become part of the ecosystem, much like APIs are today. While MCP is still evolving, the trend suggests it could become the “universal adapter” for AI integrations in the near future.
