MCP vs API: how to understand their relationship

The Model Context Protocol (MCP), developed by Anthropic, is emerging as a key method for integrating large language models (LLMs) with third-party data sources, potentially reshaping the role of APIs in AI product support. MCP consists of three components: an MCP client, an MCP server, and tools that allow the LLM to perform specific actions based on user input. While some speculate that MCP could overshadow APIs, it actually highlights the complementary nature of APIs, which provide reliable, secure, and frequent data access that scraping methods cannot match. APIs support MCP by enabling real-time data access, maintaining security through HTTPS and standardized authentication flows, and ensuring reliable data synchronization. Merge’s platform facilitates these integrations through its Unified API, offering tools for integration observability and security features like Common Model Scopes to manage customer integrations effectively.

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