Why Your Company Should Know About Model Context Protocol

The Model Context Protocol (MCP) is an open standard developed by Anthropic that enables secure, two-way connections between AI models and an organization’s data sources. Think of it as a universal adapter that allows AI systems and agents to safely access and interact with your business data, tools, and systems.

MCP addresses a critical limitation in current AI agent implementations: the isolation of AI models from the systems where a company’s data lives. Without MCP, every new data source requires its own custom integration, making truly connected, agentic systems difficult to scale. You can think of MCP as the “nervous system” that allows AI agents to perceive and act upon your business environment.

Why Should Your Business Care About Model Context Protocol?

1. Build Effective AI Agents

MCP provides the foundation for building effective AI agent frameworks — systems that can operate autonomously to accomplish complex tasks (the autonomous piece is what differentiates them from copilot or chat agents). As Anthropic notes in their research on building effective agents, the most successful agentic systems aren’t built with complex frameworks but with “simple, composable patterns.” MCP delivers exactly this kind of composable architecture.

With MCP, you can build agents that:

• Plan and execute multi-step tasks
• Use tools and APIs to take actions
• Access and reason over your organization’s data
• Adapt to changing information and requirements

2. Unlock the Full Potential of Your AI Investments

Your AI agents are only as good as the data they can access and the actions they can take. With MCP, you can connect your agents to:

• Nasuni managed files and data
• Customer relationship management systems
• Project management tools
• Development environments and code repositories
• And virtually any other data source your company relies on

Instead of AI agent systems that operate in isolation, MCP allows them to draw on your organization’s collective knowledge and act on its behalf, which ultimately leads to more accurate, contextual, and valuable outputs.

3. Simplify Integration, Reduce Development Costs

Before MCP, connecting AI Agents to your data required custom implementations for every integration point. This approach is:

• Resource-intensive
• Difficult to maintain
• Hard to scale
• Prone to security vulnerabilities

MCP replaces fragmented integrations with a single, standardized protocol. This means your development team can build once and connect to many data sources, significantly reducing implementation time and costs.

4. Enhance Security and Control

MCP was designed with security as a foundational principle. It provides:

• Explicit user consent requirements
• Clear permission models
• Granular access controls
• Transparent tool usage

This means your organization can connect AI systems to sensitive business data while still maintaining appropriate security boundaries and controls.

5. Future-Proof Your AI Strategy

As an open standard, MCP is rapidly being adopted across the AI ecosystem. Companies such as Google, Microsoft, OpenAI, Replit, and Zapier have already announced MCP support. By implementing MCP, your organization positions can benefit from this large and growing ecosystem of compatible tools and services.

Real-World Applications of AI Agents with Model Context Protocol

MCP enables the creation of powerful agentic systems that can transform how your business operates. As noted in Anthropic’s research, the most promising applications for AI agents are those that combine conversation with action, have clear success criteria, enable feedback loops, and integrate meaningful human oversight.

Example 1: Intelligent Document Processing

A legal firm could implement an MCP-enabled agent connected to their Nasuni file system that:

• Automatically processes contract documents that land in a particular share path
• Extracts key terms, deadlines, and obligations
• Cross-references with previous contracts in the system
• Flags inconsistencies or potential issues for review
• Updates their contract management database
• Generates executive summaries for the legal team

By connecting the agent directly to their secure Nasuni managed storage, the firm could reduce contract review time by up to 70% while increasing accuracy.

Example 2: Research Knowledge Synthesis

A pharmaceutical research company could build an MCP agent that:

• Scans their Nasuni stored files for research documents, lab reports, and study results
• Identifies connections between separate research initiatives
• Generates comprehensive literature reviews across their data
• Creates structured metadata for improved searchability
• Compiles custom research briefs based on specific queries
• Alerts researchers when new internal documents on a share relate to their areas of interest

This agent effectively turns their Nasuni file system into an intelligent knowledge base that actively supports research efforts rather than just storing information.

Example 3: Architecture, Engineering and Construction (AEC) Project Coordination

An AEC firm could implement an MCP-enabled agent that leverages their Nasuni file systems to:

• Monitor their BIM (Building Information Modeling) files, CAD drawings, specifications, and regulatory documents across projects
• Flag regulatory compliance concerns by comparing designs against building codes stored in their knowledge base
• Generates daily coordination reports highlighting critical issues requiring resolution
• Links RFIs (Requests for Information) to relevant drawings and specifications
• Tracks version history of all project documents and summarizes changes between iterations
• Alerts project teams when updated documents might impact their work
• Prepares documentation packages for client and regulatory submissions

By integrating with Nasuni, the agent could reduce coordination errors, shorten review cycles, and help deliver projects on time and within budget. Most importantly, the system provided continuity and knowledge transfer between different project phases and teams – a critical advantage in an industry where information silos often lead to costly mistakes.

Getting Started with Model Context Protocol

Implementing MCP within your organization is remarkably straightforward:

1. Start with pre-built servers: Anthropic provides reference implementations for a File System, as well as Slack, GitHub, Git, Postgres, and more.
2. Pilot with Claude for Desktop: Your team can begin experimenting with local MCP servers connected to Claude Desktop to test capabilities.
3. Develop custom connectors: As your needs grow, your development team can leverage the growing ecosystem of MCP servers or build custom MCP servers for organization’s specific systems.

The Business Case for AI Agents

The business value of MCP and the agents it enables comes down to several key benefits:

1. Autonomous execution: Agents can complete complex tasks with minimal human intervention, freeing your team to focus on higher-value work.
2. Enhanced productivity: By connecting AI agents to your systems, employees can accomplish more with less effort and delegate routine tasks.
3. Cost reduction: Standardized integration means lower development and maintenance costs for your AI infrastructure.
4. Better outcomes: Agents with access to relevant context and appropriate tools produce higher quality results.
5. Scalable AI operations: As your AI needs grow, MCP provides a consistent framework for adding new capabilities.

From Simple AI to Sophisticated Agents

The evolution of AI in business is following a clear progression:

1. Basic AI: Simple question-answering systems with limited context.
2. Augmented AI: Systems enhanced with retrieval capabilities for specific use cases.
3. Workflow AI: AI integrated into predefined business processes.
4. Agentic AI: Systems that can plan, reason, and take action independently.

MCP can be thought of as the bridge that enables companies to evolve from simple AI implementations to sophisticated, agentic systems that can transform how work gets done.

The Future Is Agentic

As agentic AI becomes the backbone of modern business operations, the difference between success and stagnation lies in how well your systems connect, collaborate and provide standardized access to the data required.

The Model Context Protocol isn’t just another technical standard — it’s shaping up to be the catalyst for the interoperable agentic enterprise. By leveraging MCP with Nasuni’s hybrid cloud platform, your organization can unlock the full potential of its data. Don’t just keep pace with the future, build it.