MCP solves the problem of inconsistent, ad-hoc, and insecure tool integrations in AI systems. Historically, models have relied on custom plugins, proprietary agent frameworks, or direct code execution to interact with external resources. This creates risks such as unpredictable behavior, uneven feature support, and difficulty validating what actions a model is allowed to perform. MCP replaces these patterns with a clear, auditable contract between the AI system and the environment, ensuring that each tool is explicitly defined and cannot exceed its intended scope.
Another major problem MCP addresses is the lack of standard discovery and invocation mechanisms. Without a protocol, developers must manually specify how the model learns which tools exist, what arguments they take, and when to call them. MCP introduces a uniform method for listing tools, describing parameter schemas, and formatting responses. This helps models make informed decisions about when to use tools rather than relying on guesswork from natural language descriptions embedded inside the prompt.
For vector workflows, MCP solves the problem of tightly coupling AI logic to database infrastructure. When an embedding model needs to interact with Milvus, developers often write custom Python code or API wrappers that are difficult to reuse across environments. MCP turns these operations into standardized tools that any model can access as long as the MCP server exposes them. This is especially helpful in retrieval-augmented generation systems, where frequent vector lookups must be predictable, secure, and easy to audit.