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BC
October 8, 2025The comparison oversimplifies latency effects. Function calling isn’t just “low latency”—it’s synchronous blocking, which halts model generation until the tool finishes executing. Testing this locally shows that network calls or database queries during function execution cause noticeable delays that users interpret as the model “thinking.” MCP’s transport abstraction greatly influences latency, but the table doesn’t provide specific measurements of these differences.
The benefit of “dynamic discovery” for MCP adds runtime complexity that the article overlooks. In my testing across multiple systems, dynamic tool discovery requires the model to understand new tools solely from their descriptions, without fine-tuning or cached examples. This process often works inconsistently—models tend to misuse tools they find at runtime compared to those they trained on.
The security comparison wrongly suggests that vendor function calling is less secure than MCP because it lacks “built-in governance.” In reality, function calling happens within the same process, giving full control over execution. MCP servers run as separate processes and need network communication, authentication, and session management—creating a larger attack surface, not less. When testing MCP-style architectures locally, securing server-to-server trust boundaries is more challenging than just verifying function arguments within your own process.
The “hybrid pattern” suggestion—exposing services via MCP for portability and mounting them as function calls for lower latency—introduces a maintenance burden that the article overlooks. Maintaining two integration methods for the same functionality becomes complex. In my multi-system testing environment, keeping these synchronized as services evolve presents its own engineering challenge, with changes affecting both MCP server configurations and function schemas.