MCP Vs RAG Vs Skills

Skills, MCP, and RAG are often discussed together, but they sit in different layers of the AI agent stack. Let’s say an agent is helping your team turn a messy product discussion into an engineering ticket. First, the agent needs access. That is where 𝐌𝐂𝐏 may come in.

MCP can give the agent access to tools and systems: Jira, GitHub, Google Drive, an internal database, or whatever else the workflow depends on.

So MCP answers: 𝐖𝐡𝐚𝐭 𝐜𝐚𝐧 𝐭𝐡𝐞 𝐚𝐠𝐞𝐧𝐭 𝐫𝐞𝐚𝐜𝐡?

But access alone is not enough. Just because the agent can open Jira does not mean it knows how your team writes a good ticket. Then the agent needs context. That is where 𝐑𝐀𝐆 may come in.

RAG can retrieve the product spec, previous tickets, customer notes, or internal documentation.

So RAG answers: 𝐖𝐡𝐚𝐭 𝐝𝐨𝐞𝐬 𝐭𝐡𝐞 𝐚𝐠𝐞𝐧𝐭 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐤𝐧𝐨𝐰?

But context is still not the full workflow. The agent may have the right documents and still miss how your team expects tickets to be written:

• User impact first.
• Separate the problem from the proposed solution.
• Include acceptance criteria.
• Flag unclear requirements instead of filling in the gaps.

This is where 𝐒𝐤𝐢𝐥𝐥𝐬 sit. An AI agent skill is a reusable package of instructions that teaches an agent how to perform a specific task. Think of it as turning repeated prompting into a reusable workflow.

Skills answer: 𝐇𝐨𝐰 𝐬𝐡𝐨𝐮𝐥𝐝 𝐭𝐡𝐞 𝐰𝐨𝐫𝐤 𝐛𝐞 𝐝𝐨𝐧𝐞?

Or more simply:

• MCP tells the agent what it can reach.
• RAG tells the agent what it can look up.
• Skills tell the agent how the task should actually be done.

Useful agents are not built from one layer alone.