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Model Context Protocol (MCP) is emerging as a foundational way to make AI tools more portable and consistent across different large language models (LLMs). Rather than rebuilding the same capabilities for each platform, MCP allows you to create a shared set of actions, prompts, and resources once, and then use them with multiple LLMs such as ChatGPT, Copilot, or Claude—provided they support the protocol. This approach aims to simplify how organizations connect their data and business logic to conversational AI, reducing fragmentation and improving maintainability.
At its core, MCP consists of two parts: a server and a client. The MCP server houses the logic and makes three key elements available to LLMs: prompts, tools, and resources. Prompts set instructions or tone for how the model should respond. Tools are executable actions such as checking inventory, retrieving approved vendors, or sending emails; these can be chained together to complete workflows. Resources are files or documents that ground the model’s answers in company-specific information, helping ensure responses are based on trusted data rather than general web content.
A practical example of MCP in action is a Compliance Agent built with Copilot Studio and a Dynamics 365 MCP server. In this scenario, the agent looks up items (like televisions), fetches approved vendors, cross-references certification details and expiration dates from a knowledge source, and emails vendors whose certifications expire within 30 days. It can run autonomously on a schedule and provides traceable activity logs showing each step taken. Notably, the configuration can be achieved through natural language, reducing the need for custom code.
Behind the scenes, each interaction follows a clear sequence. The user’s message reaches the LLM, which assembles context and selects relevant tools via the MCP client. The client sends a structured JSON call to the MCP server to execute the action, receives results, and then the LLM crafts the final response. While this architecture promotes modularity and reuse, it also introduces performance and security considerations. Tool calls are API requests and can be “chatty” if not designed thoughtfully, potentially hitting rate limits or affecting system performance. Security depends on how the MCP server is implemented, including how roles and permissions are enforced.
For technical teams, MCP offers a path to wrap business logic in reusable, scalable services that work across multiple AI interfaces. For business and functional users, it enables natural-language solutions to real workflows without extensive custom development. MCP adoption is still expanding, and not every LLM client supports it yet, but its emphasis on contextual, modular, and cross-platform design signals a shift in how organizations will build and deploy AI capabilities.