Practical Lessons from Deploying MCP in the Enterprise
The enterprise AI landscape is evolving rapidly, and organisations are discovering that traditional language models, whilst conversational and intelligent, have a critical limitation: they can't take action. They exist in isolation from the systems, data, and workflows that drive business value. This is where the Model Context Protocol (MCP) becomes a game-changer.
What is MCP and Its Core Principles
The Model Context Protocol (MCP) is a new open standard that's fundamentally changing how AI systems interact with real-world software. Think of MCP as the missing link between powerful language models like GPT or Claude and the actual tools and data businesses rely on every day—SharePoint repositories, customer management systems, databases, and document stores.
Traditionally, large language models have excelled at conversation and answering questions, but they couldn't act. They couldn't reach into a system to pull a real file, update a document, or run a search inside your actual data store. MCP changes this paradigm entirely.
An MCP server provides a structured way to expose a set of tools—like finding files, downloading documents, or searching through databases—in a way that language models can understand and use. It tells the AI what tools are available, what each tool does, what input it needs, and what kind of output to expect. This empowers the model to decide which tools to use to answer a user's question, make the actual call, get the result, and continue the conversation seamlessly.
The beauty of MCP lies in its modularity, security, and standardization. You don't need to build hardcoded integrations or complex prompts. It's clean, discoverable, and grows with your needs. This is what enables AI to become truly useful at the enterprise level.
The Value of MCP: From Passive Advisors to Active Participants
MCP transforms language models from passive advisors into active participants in your business processes. Imagine due diligence reviews, compliance checks, or contract workflows—tasks that are currently manual and error-prone. With MCP, AI can run these checks for you inside the systems you already use, combining the intelligence and flexibility of natural language processing with real-world action.
The risk of not adopting MCP isn't just about missing out on productivity gains—it's about falling behind in how fast your business can learn, adapt, and make decisions. Without MCP, even the most powerful AI models remain disconnected from the systems where knowledge actually lives. This means employees are stuck searching through SharePoint, clicking through documents, and performing manual tasks that could be automated.
Competitors who adopt MCP can turn natural language into real-time actions, retrieving documents, running analyses, and processing information faster, more accurately, and at scale. They develop what we call "knowledge velocity"—the ability to access and act on information instantly. Over time, this compounds into faster innovation cycles, better decision-making, and ultimately a deeper competitive moat.
Considerations for Enterprises
Most enterprises today struggle with disconnected data silos, and many organisations have logically used SharePoint as a knowledge repository for years without being able to extract all the insights from it. If you've ever tried to find information in SharePoint and couldn't locate it because the search index wasn't optimal or the information was buried ten pages deep, you understand the problem.
Without MCP, businesses are limited to surface-level Q&A with AI. A legal team might have thousands of contracts in SharePoint, but without an MCP server, the AI can't reach in to retrieve or summarise those documents automatically. Someone still has to do it manually. Similarly, a sales team might use Salesforce, but their AI system can't update records, check deals, or surface sales opportunities unless there's a direct integration—which often doesn't scale.
Legal professionals, for example, might spend up to 25% of their time searching through databases, checking documents, comparing contracts, and summarising content. This represents a huge amount of time that could be translated into money costing the business. It's not just about time—it's about paying experts to go through documents instead of using those summarised insights to provide real value.
Our 5 Big Lessons Learned from Real-World Deployments
1. Plan Your Architecture Early, Especially Security
If we could do our deployment again, we would take more time upfront to understand the best architecture for our specific use case and data model. Security became a major consideration halfway through our project, requiring us to go back and restructure our approach.
We implemented user-specific authentication tokens that come from Microsoft authentication. This means users who don't have access to specific documents in SharePoint won't be able to use those documents with the AI. It's a crucial guardrail where every team can access only the documents they're supposed to use through the AI, ensuring proper compartmentalization of knowledge across teams.
Our recommendation: establish SSE connections early to enable secure, token-based authentication. Every user connects with the server using their specific authentication token, and the MCP server respects existing access controls.
2. The Community is Growing, But Custom Work is Still Required
MCP was introduced just over a year ago, so there aren't many ready-made solutions for platforms like SharePoint or other enterprise data sources. Significant work is still required from data engineers or AI engineers to connect MCP servers to where customer data actually lives. However, the community is rapidly growing, and we're seeing more integration solutions emerge regularly.
3. Tool Design Requires Careful Consideration
Defining the tools you'll use with MCP for your specific case takes considerable time and thought. In our SharePoint implementation, we started with nine tools that can manipulate documents: retrieve documents, search documents, create documents, and even upload content through natural language interaction. The key is starting with a solid foundation that you can build upon iteratively.
4. Plan for Multi-Model Support
We wanted our MCP server to work with different large language models, which requires significant backend engineering. You need to ensure your MCP server is agnostic in how it's used by different language models, requiring careful consideration of how various models connect to your MCP server.
5. Build with Modularity in Mind
Once you've built the architecture and have basic tools working, MCP's modular nature shines. You can keep adding tools as you go, making the system more user-agnostic. Different tools can serve different customers and different data storage solutions they use. This iterative approach allows you to start with base tools and continuously improve them while reusing the foundational architecture.
Practical Experiences with SharePoint
For many organisations, SharePoint serves as the central nervous system for enterprise knowledge—housing everything from contracts and compliance documents to project files and strategic plans. It's where institutional knowledge lives, grows, and unfortunately, often gets buried. Despite its widespread adoption, many organisations struggle to unlock the full potential of their SharePoint repositories due to search limitations, complex folder structures, and the sheer volume of content.
We're currently working with a number of customers who recognise the untapped value in their SharePoint environments and are looking to leverage AI to finally make this knowledge truly accessible and actionable. These practical experiences represent real-world implementations across different industries and use cases.
Our SharePoint MCP server connects directly to Microsoft SharePoint using the Microsoft Graph API and exposes a comprehensive set of tools including searching for files, downloading documents, parsing contents, uploading or updating files, and more. These tools are made available to the AI assistant through the MCP protocol.
When a user asks a question like "Can you show me the latest signed NDA for client X?", the language model doesn't just guess—it actually calls the right tool to search SharePoint, retrieve the file, and summarise the content. This transforms SharePoint from a static document repository into an active, intelligent data source.
Our platform acts as the front-end interface where users interact naturally with the assistant, whilst the MCP infrastructure behind the scenes gives the AI real capabilities to operate on enterprise data securely, accurately, and in real-time. Because we built this with modularity in mind, we can easily extend it to meet specific client workflows such as due diligence reviews, compliance checks, or automated document processing.
The system maintains strict security through Microsoft's authentication system, ensuring that AI interactions respect existing access controls and permissions. Users can leverage fine-grained search capabilities through Microsoft Graph, enabling the AI to search documents, grab specific paragraphs from specific pages, and return precisely what users need.
Closing Thoughts
The implementation of MCP in enterprise environments represents more than just a technological upgrade—it's a fundamental shift in how organisations can leverage their existing data and systems. The lessons we've learned from our SharePoint integration highlight both the challenges and immense opportunities that MCP presents.
The key to success lies in careful planning, particularly around security architecture, understanding that some custom development work is still required, and building with modularity and extensibility in mind. Whilst MCP is still relatively new, the rapid growth of the community and the tangible benefits we've seen in real-world deployments suggest that early adopters will gain significant competitive advantages.
Organisations that successfully implement MCP will develop that crucial "knowledge velocity"—the ability to access and act on information instantly—transforming their AI from conversational tools into active participants in business processes. The question isn't whether to adopt MCP, but how quickly you can begin the journey.
