MCP, short for the Model Context Protocol, is an open standard designed to help AI systems communicate with external tools, services, and data sources in a predictable and structured way. At its core, MCP defines how an AI model can discover available tools, understand their input/output formats, and call them consistently without requiring custom integration logic for each environment. AI systems use MCP because it replaces ad-hoc integrations with a stable interface that allows models to work safely and reliably with external systems, much like how APIs standardized service-to-service communication.
In practice, AI systems rely on MCP when they need information or capabilities beyond what the model can infer from its training data. For example, a model might need up-to-date financial information, access to a user’s local files, or the ability to store and query vector embeddings. Without MCP, each of these tasks would require platform-specific code or proprietary plugins. MCP solves this by giving models a consistent “contract” describing what actions they can take, what parameters are allowed, and how responses are returned. This keeps the AI system grounded in real data rather than guessing from memory.
From the perspective of vector databases, MCP helps bridge the gap between model-generated embeddings and external storage or retrieval systems. If the model produces embeddings for similarity search, MCP allows it to call a vector database like Milvus through a tool defined in the protocol, retrieve nearest neighbors, and act on the results. This turns vector search into a first-class capability inside the model’s runtime environment, enabling use cases such as retrieval-augmented generation, semantic search, and context expansion without specialized infrastructure work for each deployment.