MCP vs. CLI vs. Skills: What Each Costs in Context (openrouter.ai)

A recent analysis has highlighted the cost differences among three approaches—Model Context Protocol (MCP), Command-Line Interface (CLI), and skills—when utilizing tools in AI models. The study evaluated these approaches using the GitHub MCP server alongside various models, including Claude Sonnet 5.5, Claude Haiku 5.5, GPT-6 Luna, and Gemini 3.8 Flash. Notably, the research found that while all three methods successfully completed the same task, the token usage varied significantly: MCP with 46 default tools consumed a staggering 59,417 tokens compared to only 1,421 tokens for CLI and 3,315 for skills. These differences underscore the potential cost implications for developers using AI tools in practical applications. This analysis is significant for the AI community as it illustrates the scalability challenges associated with employing detailed tool definitions in models. The MCP approach can offer extensive capabilities but at a higher input token cost, potentially influencing the choices developers make when deciding on AI tools. The introduction of 'Tool Search' aims to mitigate these costs by allowing models to defer loading extensive tool definitions until necessary, suggesting a path toward more efficient interaction with multiple tools. Overall, this research emphasizes the importance of optimizing tool usage in AI model deployment to balance functionality and operational costs effectively.