MCP (Model Context Protocol)

AI-driven applications depend on up-to-date contextual data to deliver accurate, relevant results—but as deployments grow, manually mapping and updating connections between APIs, databases, and third-party services rapidly becomes a maintenance burden. The Model Context Protocol (MCP) solves this by defining a uniform, extensible framework for context discovery and exchange. MCP-compliant servers and agents provide automated context synchronization, real-time updates, and frictionless integration, powering scalable, reliable AI workflows across diverse data sources.

To everyday users, the most compelling AI systems can appear “magical”—an intuitive, effortless performance that inspires trust and immersion. Achieving this outcome requires effective use of contextual data, but each model has a finite context window that limits how much contextual data they can process at once. MCP expands the landscape of retrievable content and ensures effective utilization of the context window by standardizing and dynamically distributing contextual data through a framework that is model agnostic. Just as HTTP established a universal protocol for web requests and USB-C standardized power and data delivery across devices, MCP defines a unified interface that enables AI systems to discover, exchange, and seamlessly manage contextual data across diverse integrations.

Additionally, MCP plays a critical role in empowering agentic AI systems—a branch of AI designed to proactively interact with and adapt to dynamic environments and connected systems. To be an effective collaborator or standalone contributor, an AI agent requires data that is seamlessly accessible, consistent, and standardized. However, manually ensuring this standard is met across a growing number of integrations creates what is referred to as an “N × M” problem: integrating “N” tools in an AI system (such as APIs, agents, or workflows) with “M” resources, systems, or services, resulting in immense scaling complexity. AI agents excel when they can dynamically and proactively retrieve resources without additional manual inputs, and dedicated MCP servers provide a wrapper on the retrieval process that mitigates these bottlenecks.

In practice, MCP operates using a server-client architecture—the AI system (the client) connects to an MCP server, which represents a specific tool, resource, or data source, and queries it to understand its capabilities. The MCP server responds with information about what it can do and how it can interact with the AI system, which is then stored by the model for future context. 

When a user initiates a prompt, the model dynamically queries its network of MCP servers, each representing a different set of functionalities. The model determines the most appropriate connections to draw from and then retrieves the contextual data needed to respond effectively—without the user needing to explicitly specify a source. This modular design empowers developers to build customized MCP servers for specific use cases or adopt pre-built versions from the open-source community.

Another key advantage MCP contributes to scalability is dynamic discovery—continuously scanning for changes across all MCP servers so new capabilities are automatically integrated and structural updates to sources don’t disrupt workflows or break existing connections.

MCP has many compelling use cases and applications. To name a few:

• Compliance – MCP servers can standardize queries across siloed regulatory bodies, ensuring seamless integration of compliance frameworks across systems. MCP’s dynamic discovery is especially useful in this ever-changing landscape to ensure actions align with the latest regulations.
• DevOps – MCP unifies structured data sources like CI/CD pipelines, testing frameworks, and monitoring tools into a unified workflow with contextual knowledge from each step to maintain continuity.
• Coding Companion – MCP’s contextual awareness across multiple repositories and application layers can be a powerful tool for developers as it integrates real-time suggestions that reflect best practices across the entire project without ignoring critical dependencies.

Retrieval-augmented generation (RAG), a technique used to augment a base AI system or AI model with contextual data, shares some similarities with MCP, but the two differ in operation and scope. Whereas both integrate contextual data to produce a more relevant and accurate output, RAG is best used for unstructured data use cases such as attaching web pages, PDFs, or other external documents as part of targeted lookups.

In contrast, MCP is better suited for wrapping the data retrieval process across large, structured data sources like customer relationship management (CRM) systems, financial reports, or other enterprise databases. Considering these complementary strengths, MCP and RAG can be very effective operating in tandem. For example, MCP could retrieve structured data from a CRM to provide RAG with standardized and normalized input data. RAG could then process this input data alongside unstructured sources such as customer emails, support chat logs, or relevant news articles. This collaboration allows AI systems to harness vast expanses of structured data while preserving the user-defined context most relevant to their needs.