Model Context Protocol (MCP)

What is Model Context Protocol?

MCP (Model Context Protocol) is an open-source standard that enables AI applications to seamlessly connect with external systems. Through MCP, large language models (LLMs) such as Claude, ChatGPT, or Microsoft Copilot can securely access data sources (e.g., local files, databases), utilize tools (e.g., search engines, calculators), and integrate with cloud services (e.g., workflows, decision tasks). Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect electronic devices, MCP provides a standardized way to connect AI applications to external systems.

Why Does MCP Matter?

Real-time Data Access

Unlike static training data, MCP enables AI assistants to fetch current information in real time, making them more useful for tasks requiring up-to-date data like checking schedules, accessing live databases, or getting current market information.

Standardization and Interoperability

MCP creates a universal standard for connecting AI assistants to external data sources and tools. Instead of building custom integrations for each data source, developers can use a single protocol that works across different systems, reducing development time and complexity.

Enhanced AI Capabilities

By providing secure access to real-time data and external tools, MCP enables AI assistants to access current information beyond their training data, Interact with databases, APIs, and other data sources, use external tools and services during conversations, and provide more accurate, up-to-date responses.

Security and Control

MCP implements robust security measures through controlled access to external resources, permission-based systems for tool usage, secure communication channels, and audit trails for external interactions.

Flexibility for Developers

The protocol allows developers to create custom integrations with their specific data sources, build specialized tools for their AI assistants, and maintain control over what data and functions the AI can access.

How Does Trisotech Utilize MCP?

Workflow and Decision services cataloged in the Trisotech Service Library (BPMN Workflows, DMN Decision Tasks) can be accessed through an MCP client generated by Trisotech. Trisotech provides a unique client URL for each supported Service in the Service Library, which users can then register with the LLM of their choice. What MCP brings to this integration is a standardized way for LLMs to connect with these services while relying on deterministic decision and process tools. This ensures that when AI systems invoke these workflows or decisions, the actions are not only consistent and repeatable but also explainable and traceable, providing a foundation of governance and trust that traditional LLM interactions often lack.

Learn More

The MCP specification provides a standardized way for applications to share contextual information with language models, create live connections to databases, APIs, and cloud services, expose tools and capabilities to AI systems, and build composable integrations and workflows.