MCP vs tool calls for AI agents: which is better?

When building AI agents with third-party API integrations, developers can choose between custom tool calls and Model Context Protocol (MCP) servers, each offering distinct advantages depending on the use case. Custom tool calls, which involve designing, authenticating, and executing API interactions directly, provide greater reliability, lower token costs, and per-user authentication, making them ideal for production environments within customer-facing SaaS products. In contrast, MCP servers, which standardize tool discovery and execution over JSON-RPC, are faster to set up and more suitable for prototypes, internal agents, and developer tooling, especially when speed is prioritized over individual request costs. MCP servers offer quick integration by exposing tools from connected servers, although they come with challenges like increased context consumption, reduced tool-selection accuracy, and limited control over authentication and code handling customer data. For comprehensive control and security in production, custom tool calls are recommended, but both approaches can be combined to leverage the strengths of each, particularly when utilizing platforms like Nango to streamline the creation and management of these tool calls.

No tracked trend matches for this post yet.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.