Glossary

Model Context Protocol

Model Context Protocol (MCP) is an open-standard framework that enables AI models to interact securely with external data sources and software tools. By providing a universal interface, it allows large language models to access local files, databases, and internal APIs, effectively bridging the gap between isolated AI systems and the fragmented data environments of modern enterprise software.

The protocol addresses the persistent challenge of data silos in AI integration. Previously, connecting an AI agent to specific business tools required custom, proprietary connectors for every unique integration point. MCP standardizes these connections, allowing developers to build a single integration that works across multiple AI platforms. For practitioners, this shift reduces technical debt and maintenance overhead, as it decouples the AI model from the underlying infrastructure, facilitating more robust and scalable data-driven workflows across diverse software ecosystems.

In practice, MCP operates through a client-host-server architecture. The AI application acts as the host, connecting to MCP servers that expose specific data or functionality via standardized endpoints. To implement this, developers configure servers to provide resources, prompts, and tools that the AI can invoke dynamically. Users should monitor for the adoption of MCP-compliant connectors within their existing tech stacks, as this standard simplifies the process of granting AI agents read-write access to internal databases and third-party services without custom middleware.

Last updated: 2026-08-26