Agent Skills vs Tools: What Actually Matters

The debate between "tools" and "skills" in the agent ecosystem underscores a significant architectural decision rather than a mere terminological issue. Tools are executable functions that perform actions in the world, while skills provide contextual expertise that informs how agents approach tasks. Companies like Anthropic, OpenAI, and LangChain have different approaches regarding this distinction, with Anthropic emphasizing the separation by using Model Context Protocol (MCP) for tools and prompt-based skills for expertise, whereas OpenAI and LangChain do not formally distinguish between the two. The choice between a tool-heavy or skill-heavy architecture affects agent intelligence, security surfaces, and token economics. Tools are necessary for executing actions and require careful authentication and authorization, while skills enhance domain-specific problem-solving but cannot perform actions independently. The industry's focus should shift from terminology debates to building authentication-first infrastructures that enable agents to operate securely in production environments, as exemplified by Arcade's approach, which prioritizes secure OAuth management across multiple services to facilitate real-world applications.