Model Context Protocol (MCP)
Description
As organizations race to adopt Generative Artificial Intelligence (Gen AI), they face a connectivity problem: powerful AI models are often isolated from the business data they need to be useful. Creating custom connectors for every database, file system and content platform is inefficient and difficult to scale. The Model Context Protocol solves this by establishing a standardized way for AI applications (clients) to communicate with data sources (servers).
MCP operates like a "USB-C port" for artificial intelligence. Just as a USB port allows any device to connect to a computer without specialized wiring, MCP allows any compatible AI assistant to plug into an organization's knowledge base. It structures this interaction around three core capabilities: resources (data the AI can read), tools (functions the AI can execute) and prompts (pre-defined templates). This standardization is critical for the rise of Agentic AI – systems that do not just chat but actively perform tasks. By using MCP, organizations can expose their Component Content Management System (CCMS) or technical documentation to AI agents. The protocol ensures that the AI receives context-rich, structured content rather than unstructured noise. This reduces hallucinations and ensures that answers are grounded in approved, governed content.
Example use cases
- Support: Connect AI assistants to a CCMS to retrieve precise troubleshooting steps.
- Workflows: Enable AI agents to perform multi-step tasks, such as looking up product IDs.
- Retrieval: Use MCP to power Retrieval Augmented Generation (RAG) pipelines.
- Operations: Allow AI tools to read and analyze content structure for metadata suggestions.
- Integration: Connect coding assistants to internal documentation servers.
Key benefits
RWS perspective
RWS views the Model Context Protocol as a vital enabler of the intelligent enterprise. We recognize that an AI agent is only as good as the information it can access. That is why Tridion Docs, our intelligent content platform, supports MCP to bridge the gap between structured content and Agentic AI.
By acting as an MCP server, Tridion Docs allows AI assistants to "see" and "read" high-value DITA content with its semantic structure intact. Instead of guessing, the AI retrieves specific topics, safety warnings and procedures directly from the source of truth. This approach combines the generative power of Large Language Models (LLMs) with the governance and precision of structured content.