What is Model Context Protocol (MCP)?
Model Context Protocol (MCP) is an open standard that enables AI assistants to securely connect to external data sources and tools in a controlled way. It serves as a bridge between AI systems and the various enterprise applications, databases, and content repositories they need to access to provide intelligent assistance.
MCP solves a fundamental challenge in AI implementation: while AI assistants are powerful, they're often isolated from the specific data and tools organizations need them to work with. The protocol provides a standardized interface that allows any compatible AI assistant to interact with external resources without requiring custom integrations for each data source.
What is MCP used for?
MCP is used to connect AI agents to enterprise systems and data sources, enabling agentic AI experiences. For example, technical documentation teams use MCP to connect AI assistants to their Component Content Management System (CCMS). This allows the AI to search structured content, retrieve specific topics based on user queries, and guide users through complex procedures using the organization's existing documentation.
In customer support scenarios, MCP enables AI agents to access product databases, customer records, and troubleshooting guides simultaneously, providing comprehensive assistance that goes beyond simple question-answering to actually helping users complete tasks.
MCP organizes external capabilities into three main categories: Tools (functions the AI can execute), Resources (data sources the AI can read from), and Prompts (reusable templates and instructions).
Why is MCP useful?
MCP transforms AI assistants from isolated text generators into intelligent systems that can interact with real enterprise data and workflows. It eliminates the need for organizations to rebuild their existing systems or migrate content to AI-native formats. Instead, MCP creates a standardized bridge that preserves existing investments while enabling advanced AI capabilities.
The protocol enables better interoperability across the AI ecosystem, reducing duplicated development efforts. For organizations with structured content like DITA, MCP allows AI systems to leverage the semantic richness and task-oriented design that technical writers have carefully built into their documentation systems, turning static content repositories into interactive knowledge bases.
