On HPE Discover Las Vegas 2025 HPE unveiled a groundbreaking agentic AI framework for hybrid operations. By seamlessly integrating agentic AIOps across nearly every infrastructure layer, HPE is redefining the GreenLake cloud as an agentic-AI-powered hybrid cloud. Central to this innovation is the Model Context Protocol (MCP), an open standard that facilitates seamless collaboration between AI agents and external systems. MCP enables multiple HPE AI and data platforms to streamline data accessibility and interaction, empowering AI-driven applications to efficiently deliver smarter, more reliable insights and operational excellence across hybrid environments.
What is Model Context Protocol (MCP)
The Model Context Protocol (MCP) refers to an open standard introduced by Anthropic in November 2024 that designed to facilitate communication, collaboration, and integration between AI agents, large language models (LLMs), and external systems such as data sources, tools, or applications. It provides a structured framework for enabling multi-agent systems to interact effectively, share context, and access precise and relevant information, thereby enhancing their ability to autonomously perform complex tasks.
The Model Context Protocol (MCP) enables AI agents to access and share contextual information seamlessly, ensuring that all agents in a multi-agent system are aligned and working with the most relevant data for their tasks. This shared context allows agents to collaborate more effectively while reducing redundancy or miscommunication.
MCP also provides a standardized way for AI systems to integrate with external data sources, tools, or APIs, eliminating the need for custom-built integrations for each use case and streamlining development to ensure compatibility across diverse systems. It supports scenarios where multiple AI agents collaborate to solve complex problems by offering a unified protocol for communication, allowing agents to exchange data, insights, and tasks efficiently. For instance, one agent might specialize in data retrieval while another focuses on analysis, and MCP ensures both can interact seamlessly.
Furthermore, MCP simplifies access to structured and unstructured data, enabling AI agents to retrieve, process, and utilize information from various sources, which enhances their capabilities and makes them more informed. By providing a structured and standardized protocol, MCP reduces the complexity and overhead associated with inter-agent communication or interactions between AI systems and external tools, leading to faster and more efficient decision-making.
Benefits of MCP in Agentic AI
The Model Context Protocol (MCP) enhances AI systems by enabling them to access richer and more precise contextual data, allowing agents to make better decisions and produce more accurate results. It facilitates scalability by simplifying integration and communication between agents and external sources, making it easier to deploy and manage large AI ecosystems. The open standard nature of MCP also provides flexibility and extensibility, allowing developers to extend its functionality for specialized use cases, driving innovation and adaptation across diverse AI applications. Additionally, MCP improves operational efficiency by reducing the need for custom solutions, lowering development time and costs, and enhancing overall system performance.
You can compare it to the USB interface that is used in consumer space to connect multiple devices to personal computers. Without having such universal connector, we would need to have multiple physical interfaces to connect various devices from different vendors. USB solves this issue, and we can easily connect any device to our computers. It is the same with MCP but instead of personal computers we have MCP clients (AI agents) connected to multiple MCP servers (other agents, applications or data sources).
HPE platforms integrated with MCP
HPE integrates the Model Context Protocol (MCP) into its platforms to support agentic AI, multi-agent collaboration, and enhanced data accessibility. MCP is a foundational technology for HPE’s GreenLake Intelligence and HPE Private Cloud AI, facilitating secure, context-rich, and standardized communication between AI agents across hybrid IT infrastructure. It promotes interoperability, enables autonomous and collaborative operations, and supports seamless integration with both HPE and third-party solutions. MCP will be supported by multiple HPE AI and storage products, starting with the HPE Alletra Storage MP X10000 for agentic, AI-powered storage. It is a foundation for HPE's new agentic AIOps platforms and unified hybrid cloud management software.
HPE GreenLake Intelligence, a hybrid cloud platform, incorporates MCP to facilitate collaboration between AI agents managing IT environments, automating workload optimization, resource allocation, and operational insights. HPE AI Essentials, a container-based software platform for AI/ML workloads which is integral part of HPE Private Cloud AI, leverages MCP to streamline workflows, enable contextual data sharing, and support efficient multi-agent collaboration for analytics and machine learning tasks.
HPE Aruba Networking integrates MCP to enable AI-powered network optimization and IoT device management, allowing agents to analyze network traffic, predict issues, and optimize performance.
Finally, HPE Superdome Flex, an enterprise-scale platform for mission-critical workloads, uses MCP to enable AI agents to interact with large-scale datasets and share real-time contextual data for precision and efficiency.
Together, these platforms—HPE Alletra Storage MP, GreenLake Intelligence, HPE Private Cloud AI, Aruba Networking, and Superdome Flex—demonstrate the versatility of MCP in driving AI innovation across hybrid cloud, edge, and enterprise environments.
HPE Alletra Storage MP X10000 and integration with MCP
The HPE Alletra Storage MP X10000 is a cutting-edge storage solution designed for unstructured data, with MCP acting as a server for multi-agent collaboration and providing enriched contextual information to AI applications. The HPE Alletra Storage MP X10000 is the industry's first agentic storage solution equipped with Model Context Protocol (MCP) servers designed for multi-agent collaboration.
The HPE Alletra Storage MP X10000 goes beyond simply storing unstructured data—it automatically enriches metadata through a built-in data intelligence engine, making the data ready for AI processing in place. Leveraging MCP, the HPE Alletra Storage MP X10000 can provide precise contextual information to other applications or agents via an MCP server. This transforms the data into a valuable resource, enabling more accurate and reliable AI insights and predictions. A detailed description of HPE Alletra Storage MP X10000 can be found here.
Benefits of using MCP on HPE Alletra Storage MP X10000
- Accelerate AI workflows
- Enhance data accuracy
- Improve data compliance and governance
- Enable visibility to intelligent data
Use cases
The Model Context Protocol (MCP) plays a critical role in enabling agentic AI, where autonomous AI agents collaborate, interact with external systems, and access context-rich data to perform complex tasks. Below are some key use cases of MCP in the context of agentic AI.
- Multi-Agent Collaboration for Problem Solving - in a complex system, MCP enables agents to share information, divide tasks, and collaborate to solve problems. For example, in supply chain optimization, one agent might focus on inventory data while another handles logistics.
- Integration with External Tools - MCP allows AI agents to interact with external systems like databases, APIs, or IoT devices. For instance, an AI-powered customer service system might use MCP to connect agents to CRM platforms and pull relevant customer information.
- Dynamic Contextual Data Retrieval - in scenarios like real-time decision-making, MCP ensures AI systems can retrieve the latest and most relevant data from external sources, such as live feeds or updated reports.
- AI-Driven Automation - MCP enables streamlined communication between agents responsible for different parts of an automation workflow, such as data preprocessing, analysis, and action execution.
The use cases go far beyond those mentioned above. Some of them are:
- Enhanced Decision-Making: MCP helps AI agents access precise data to make informed decisions, like tailoring financial investment strategies.
- Task-Oriented Agent Collaboration: MCP enables agents to divide and collaborate on tasks, such as optimizing logistics and predicting demand.
- Contextual AI Insights: MCP enriches raw data with metadata, improving the accuracy of insights, such as in scientific research analysis.
- Advanced AI-Powered Search: MCP improves search by enabling access to contextual data, such as retrieving relevant legal case information.
- Personalization and Recommendation Systems: MCP allows AI agents to analyze data for personalized recommendations, such as in e-commerce platforms.
- Real-Time Monitoring and Alerts: MCP powers real-time monitoring systems, enabling agents to detect anomalies and recommend maintenance.
- Collaborative Creativity: MCP supports creative AI processes, such as agents collaborating to draft, edit, and align content to user preferences.
- Federated AI Systems: MCP facilitates secure collaboration between distributed AI systems, like sharing insights in federated learning without exposing sensitive data.
- Data Enrichment and Metadata Management: MCP automates metadata tagging for AI-ready data, such as in HPE Alletra Storage MP systems.
- Context-Aware Automation in IoT: MCP allows agents to collaborate in smart IoT systems, like managing lighting and security in smart homes.
- AI-Powered Hybrid Cloud Operations: MCP helps agents manage hybrid cloud environments by optimizing workloads and automating infrastructure tasks.
Summary
The Model Context Protocol (MCP) unlocks powerful use cases in agentic AI by enabling structured communication, seamless integration, and contextual data sharing among AI agents, external systems, and tools. Whether it's multi-agent collaboration, automation, personalization, or enhanced decision-making, MCP provides the foundation for smarter, more efficient, and scalable AI-driven systems. MCP is an open standard that simplifies integration between large language models (LLMs) and external data sources or tools. Rather than creating custom integrations for each AI use case, MCP offers a standardized approach for AI applications to access, share, and interact with data. This standardization enhances AI efficiency and intelligence by minimizing communication overhead and improving data accessibility.
PiotrDrag
HPE Storage for Unstructured Data and AI Category & Business Development Manager for Central Europe. Passionate about primary storage, data protecion, Cloud Computing, scale out storage systems and Internet of Things.
