If you’ve started building out an AI strategy, you’ve probably run into the acronym MCP, but what is it, and what does it have to do with your business? Questions like “What is MCP in AI?” and “How does MCP fit into my tech stack?” are becoming more common as companies move forward with AI adoption.
MCP, or Model Context Protocol, could change how much value you actually get from your AI investments. Let’s dive into what MCP really means and why you should include it in your AI strategy.
What is MCP?
Model Context Protocol (MCP) is a tool that enriches and adds context to data by enabling AI systems to communicate with outside data. They can do this through existing APIs (Application Programming Interfaces), building on top of SQL databases, or other sources or files. It acts as a bridge and translator between information, allowing AI models to access and catalog data from multiple sources. Quite literally, it is the general protocol that provides context and business information to your AI.
How does MCP Work?
The three components of MCP are the host, the client, and the server. Together, these make up the MCP ecosystem and allow you to integrate multiple data sources with your AI.
The MCP host is the part of the AI that the user interacts with. In regard to how eOne Solutions is using MCP for better data integration, the two components we focus on most are the clients and the servers.
MCP clients maintain the connection between the host and server. It takes the request from the host and passes it on to the server so that the server can do its job. MCP Servers take those requests and complete the tasks, then send the output back to the client. They expose capabilities to the AI application so that it can act on your data with a full understanding of your business information.
MCPs and LLMs
Connecting an API, database, dedicated connector, or other source to LLMs (Large Language Models) provides your AI with the context it needs to provide accurate results. But what happens when you have multiple apps or data streams you need to connect to your AI? That’s where MCPs come in.
MCPs connect to multiple sources and translate them into a single, standard language that the AI can understand. This enables the LLM to link directly to one or more data sources, giving your AI the context it needs to understand your data.
Popular LLMs, like ChatGPT, Claude, Gemini, and Microsoft Copilot each bring different strengths to the table, but not every MCP is accessible by every LLM. For example, Microsoft has built its Copilot system so that it easily integrates with other Microsoft platforms, but doesn’t always connect to outside MCPs, promoting vendor lock-in practices. Because of this, it’s important to keep in mind LLM and MCP connectivity limitations to ensure you can take the actions you need to.
Within the LLM, MCP tools allow you to invoke (or tell) the server to trigger actions, like get specific data, run a process, or calculate script. This demonstrates how MCPs and LLMs work together to make it possible for an agent or end user to utilize the data and functions from the source within the AI experience.
Why are MCPs Important?
The biggest roadblock to successful AI data integration and implementation is the lack of access to quality data. LLMs have the power to streamline workflows and create content efficiently, but only if trained correctly or given the right tools to communicate with data. The creation of MCPs solve two very important issues:
- The MxN Problem: Prior to MCP, if you had M different AI models and N different tools or data sources, you needed to write MxN custom integrations. Every model needed its own connector for every tool. That’s a ton of duplicative work needed every time a new AI model is released.
- The Context Gap: AI models are powerful, but they are blind to information outside of the data they’re trained on. Before MCP, AI tools could not see live systems, databases, or your files without brittle AI integrations that were hard and expensive to maintain.
By utilizing MCP, you are giving your AI model the ability to perform more complex tasks with a larger database. Now, AI can perform multi-step tasks to give the end user a more useful output.
Benefits of Integrating with MCP
The whole point of using MCP is to give your AI context and insight into your business data. But what’s the difference between copying and pasting your information into a spreadsheet and simply giving that to your AI, instead of utilizing MCP? Both ways give your AI insight into your business data, so why choose MCP?
By utilizing MCP, you can ask more of your AI. MCP connectivity allows for:
- Real-time data access and the ability to experiment more quickly
- More security measures to keep your data safe
- Two-way action & more efficient collaboration between your team and AI
- Deep integration for more comprehensive connections
If you want to start utilizing MCP to improve how your AI works for your team, you need to find an MCP server designed specifically with data integration (and data privacy) in mind.
MCP Integration with eOne Solutions
Many companies are starting to develop their own MCP servers. If you utilize multiple applications, it can get complicated fast.
eOne Solutions has wrapped Popdock into its own MCP server, creating PopdockAI. Now, you can access your data from whatever platform you’re working on, and empower your AI agent of choice to act on that data.
PopdockAI MCP Integration
AI is only effective when it understands your business systems and data. Purpose-built MCP tools can help solve:
- Security Risks: Redefine security guardrails with built-in security layers that give you granular control for who can access which data and provide in-depth auditing capabilities to see how that data is used.
- Business Logic Knowledge Gaps: Our MCP tools respect existing business logic, giving your AI the information they need to produce accurate results.
- Context Window Limits: Quit wasting tokens and time with generic APIs. Our specialized tools allow for clearer choices, cleaning up space for actual reasoning, and producing more consistent outcomes.
- Control & Auditability Issues: Know exactly what actions are being taken and by whom. Our specialized MCP tools ensure AI can’t act without specific consent, preventing unauthorized actions.
PopdockAI’s MCP server integrates into your existing systems and AI stack. Individual logins let you control each user’s data permissions, and your team can act on insights without leaving their current application.
The Future of AI Integration
Now you should have a better understanding of what MCP is and what its capabilities mean for the future of AI. The tools are there, and PopdockAI can help you bring the power of AI functionality into your workflows. With PopdockAI’s MCP server, you’ll be able to:
- Easily connect any MCP-compatible AI platform of your choice (Claude, ChatGPT, Mistral, Microsoft CoPilot, etc.)
- Make one simple connection to access the data your AI needs.
- Get answers and take action powered by your secure, clean, and up-to-date data.
Have questions about PopdockAI’s MCP capabilities? Please contact the eOne Solutions team to learn more.