Model Context Protocol (MCP) is the new standard of AI enablement. It facilitates secure access to external data and tools between AI systems and AI agents in real time. Rather than operating in isolation, MCP allows AI to work with context, connecting to accurate, live information from verified data sources.
For businesses leveraging AI to automate data-heavy workflows like insurance verification, consumer data collection, or document processing, MCP represents a major leap forward. It enables AI to perform in a way that’s not just fast but also reliable and intelligent.
An MCP workflow connects AI systems with the external data they need to operate effectively. Instead of feeding static data into a model with multiple tools or even manually uploading documents, MCP creates a structured connection between the AI model and real-world data sources in one unified function.
For example, imagine an auto lender is using an AI agent to verify a borrower’s insurance policy. Without MCP, the AI might rely on outdated or incomplete data, multiple APIs relying on various integrations, and convoluted requests from end users. With MCP, that same AI agent can securely access the most recent insurance information directly from trusted sources in one instant action, enabling a swift, accurate decision.
This connectivity is what sets MCP apart. It doesn’t just make automation possible; it makes it even quicker, more reliable, and exponentially powerful.
Traditional automation systems follow predefined rules. But agentic workflows with MCP introduce something more sophisticated: intelligent decision-making.
Agentic workflows use AI “agents” capable of reasoning, making requests, and interacting with other systems or tools autonomously. The MCP acts as the secure communication layer that allows those agents to interact safely and contextually with external data or applications.
Here’s what that looks like in practice:
This combination of MCP and agentic workflows allows businesses to scale automation while maintaining the integrity and accuracy of their data. It’s the difference between running an isolated script and orchestrating a connected, intelligent system that learns, updates, and adapts.
Businesses everywhere are experimenting with AI, but few have achieved full integration between AI tools and their existing systems. That’s largely because most models lack connectivity. They can analyze and predict, but they can’t act without the right data infrastructure in place.
MCP agent workflows solve this by providing a standard framework for communication between AI and external data. Instead of relying on dozens of APIs or complex custom integrations, MCP provides a unified protocol that ensures AI agents have secure, real-time access to exactly what they need.
For organizations using MCP, like from MeasureOne, this means AI can now:
As MCP becomes more widely adopted, we’ll see an entirely new class of MCP + AI workflows take off; ones where AI systems operate not just as tools, but as active participants in business operations.
These workflows will make it possible for businesses to connect AI agents to live databases and APIs without manual setup, run verifications or validations automatically, triggered by business events, reduce friction across departments that rely on data accuracy, and even build end-to-end automation systems that continuously learn and improve
MeasureOne’s platform is already built to support this kind of AI-driven connectivity.
By offering secure, automated access to verified data, including insurance, income, tax, and education information, MeasureOne provides the trusted data backbone for any consumer verification workflow. And when combined with MCP, MeasureOne’s data connectivity makes AI-powered workflows not only possible, but practical and enables businesses to move from testing AI tools to building AI ecosystems.
Get started with MeasureOne today to explore MCP tech.