Glossary

MCP (Model Context Protocol)

MCP is an open standard that enables AI models to securely connect with external data sources, tools, and local development environments. By providing a universal interface for data exchange, it eliminates the need for custom, proprietary integrations, allowing AI agents to interact consistently with diverse software ecosystems, databases, and internal enterprise systems.

The emergence of MCP addresses the fragmentation inherent in AI-driven workflows. Previously, connecting an AI agent to specific business tools required bespoke API development and maintenance for every individual integration. This protocol standardizes the communication layer, allowing developers to build a single connection that functions across multiple AI platforms. For technical teams, this shift reduces the overhead of managing disparate data pipelines and ensures that AI agents can access real-time, context-rich information without compromising security or architectural integrity.

In practice, implementing MCP involves deploying a host application that communicates with local or remote servers via standardized JSON-RPC messages. Practitioners should focus on configuring their data sources as MCP servers, which then expose specific resources and tools to the AI client. When evaluating this architecture, monitor for latency in data retrieval and ensure that access control policies are strictly enforced at the server level. This approach allows for modular, scalable AI deployments that remain agnostic of the underlying model provider.

Last updated: 2026-08-31