In a recent internal sharing, I posed a choice question: Based on current Claude Agent ecosystem practices, which of the following descriptions of the relationship between Skill and MCP is most accurate?

The options include Skill completely replacing MCP, MCP completely replacing Skill, they are substitutes, they are fundamentally complementary, and uncertain. The correct answer is the fourth option: Skill and MCP are fundamentally complementary.


A user explained these concepts using a company operation analogy: Agent is the brain, Sub-agent is the hands, Skills are experience, and MCP is the language for external communication. This image clearly shows the relationships between the components.

**The Essence of Skill is Workflow Substitution**

Skill injects expert instructions, best practices, and example dialogues for specific domains through models. It’s more like giving AI a toolbox, allowing the model to decide how to combine and use them, much more flexible than hard-coded workflows. A developer noted that Skill is essentially reusing the processes you need to repeat each time, combining AI intelligence with SOP compliance.

**MCP Provides Standardized Tool Calls**

MCP, as a standardized protocol, allows models to securely call tools and data sources exposed by remote servers. It is the entry point for external resources and tools, providing a standardized way for Agents to interact with the outside world.

**Complementary, Not Competitive**

As Xiangma said, Skills are completely unnecessary to compare with MCP—they are fundamentally two different things. MCP can perfectly be a toolkit for Skills. Skills eliminate workflows, finding a middle ground between dynamic planning and process-oriented approaches.

A developer added that Skills are like giving AI a toolbox, letting it decide how to combine and use the tools, much more flexible than hard-coded workflows. This design philosophy aligns more closely with the spirit of LLMs: throw in some text, and let the model handle it.

**Considerations in Practical Applications**

While theoretically appealing, some developers raised questions: Are there really that many use cases for Skills? After all, it’s not as simple as running tests, and it’s impossible to guarantee that workflows will be the same every time. This skepticism reflects the complexities that need to be considered during practical implementation.

Another advantage of Claude Skills is its openness. Essentially, it’s just Markdown with a bit of YAML metadata, plus optional resources, reference documents, and executable scripts. And it’s not limited to Claude—the Codex CLI or Gemini CLI can also read and use it.

This design makes Skills highly portable, allowing developers to easily share and reuse various skills. From installing a PDF processing skill to complex enterprise workflows, Skills provide a relatively lightweight implementation approach.

In the rapidly evolving world of AI Agents, understanding these core concepts is crucial. Skill and MCP are not either-or choices but two dimensions that need to be considered simultaneously when building intelligent agents.