# MCP vs Skills > Source: > Published: 2026-09-09 11:30:00+00:00 A common question that I still see flying around is that: will skills replace MCP? I understand why people ask. Both can extend what an AI agent can do. A skill can include scripts that call APIs, while an MCP server can expose tools that call those same APIs. They can look like two ways of solving the same problem. But I don’t think one is going to kill the other. They sit at different layers of the architecture, and they become more useful when we combine them. ## Start with the agent Before comparing MCP and skills, it helps to look at what an AI agent needs to do useful work. A model can reason about a request and generate a response. But reasoning alone is not enough, I also explained more detail in [Why does MCP matter?](https://codeaholicguy.com/2025/06/14/why-does-mcp-matter-a-deep-dive-for-engineers/). The agent also needs to know: - What process should I follow? - What information can I access? - What actions am I allowed to take? - How should I use those capabilities safely? Skills and MCP help answer different parts of those questions. The simplest way I think about it is: A skill teaches the agent how to do something. MCP gives the agent a standard way to access something. That distinction is not perfect, but it is a useful place to start. ## A skill is the instruction layer An agent skill is usually a folder containing a `SKILL.md` file. That file describes what the skill does, when the agent should use it, and what process it should follow. A skill can also include: - Reference documents - Templates - Examples - Scripts - Other supporting assets For example, a spreadsheet skill might tell the agent how to inspect a workbook, preserve formulas, format the output, and verify the final result. It could also include scripts for manipulating spreadsheet files. The important part is that the agent gains a method to do things. Skills are also designed to load progressively. The agent can first see a skill’s name and description, then load its full instructions only when the task requires them. This lets an agent have access to many specialized workflows without putting every instruction into its context at once. In many tools, skills live on the local filesystem. This makes them easy to inspect, edit, commit to Git, and customize for a project, and some time it fragmented between different tools. That also the reason why I built [AI DevKit skills management](https://ai-devkit.com/docs/7-skills/), so that we can manage skills consistently between different tools. ## MCP is the integration layer MCP, or Model Context Protocol, provides a standard way for an AI application to communicate with external tools and data sources. Instead of every AI client building a custom integration for GitHub, Google Drive, Slack, or an internal database, it can connect to an MCP server. The server advertises the capabilities it supports and accepts structured requests from the client. This separation matters from an engineering perspective. The team operating the MCP server can update its implementation, fix bugs, change an internal API, or improve how it talks to the underlying service. Clients do not need to understand those internal changes, provided the MCP contract remains compatible. MCP can also provide a more suitable boundary for concerns such as: - Authentication and authorization - Input and output schemas - Centralized deployment - Logging and observability - Live data access - Shared capabilities across multiple agents MCP servers are not necessarily remote. MCP supports local communication between processes as well as communication with remote servers over HTTP. [MCP architecture overview](https://modelcontextprotocol.io/docs/2026-07-28/learn/architecture). So the difference is not simply local versus remote. I personally believe it is closer to instructions versus integration. ## Where the overlap becomes real The boundary gets blurry when a skill includes executable scripts. Imagine a skill containing a script that calls the GitHub CLI. If the user has already authenticated the CLI, the agent can use that script to open an issue. An MCP server could expose an `open_issue` tool that produces the same result. In that specific case, a skill script and an MCP tool may appear interchangeable. For a local coding agent running on your laptop, they sometimes are. This is why the “skills replace MCP” argument is not completely unreasonable. A skill can be faster to create and easier to distribute. If the agent already has filesystem and command-line access, adding an MCP server for one small operation may create more infrastructure than the task needs. But the tradeoff changes when the capability must be shared across many agents or users. If you need centrally managed authentication, consistent permissions, production monitoring, live data, and an implementation that can be updated in one place, an MCP server becomes much more valuable. ## They solve different parts of the same problem Here is the comparison I find most useful: | Aspect | Skill | MCP | |---|---|---| | Main purpose | Teach the agent how to perform work | Connect the agent to tools and data | | Typical contents | Instructions, references, templates, scripts | Tools, data, resources, schemas, and authorization | | Common deployment | Local or bundled with the agent | Local process or remote server | | How it changes | Update and redistribute the skill | Update the server behind a compatible contract | | Best suited for | Repeatable workflows and specialized guidance | Live data, shared services, and external actions | | Main dependency | Model reasoning and execution environment | Protocol support and server availability | These differences also show why they fit together so naturally. A skill might tell an agent how to investigate a customer complaint: 1. Find the customer and confirm their identity. 2. Retrieve recent support tickets. 3. Check relevant transactions. 4. Compare the evidence. 5. Summarize the likely cause without exposing sensitive data. MCP servers could provide access to the support system, transaction database, and customer records. The skill owns the workflow. MCP provides the connections. Without the skill, the agent has tools but may not know the right process. Without MCP, the agent understands the process but may not have access to the information needed to complete it. If my sharing is helpful to you, subscribe to my blog. I share what I learn while building real systems with AI in the loop. You can also follow me on [X](https://x.com/codeaholicguy/) or [Threads](https://www.threads.com/@codeaholicguy) for more thoughts and ongoing experiments. ### Discover more from Codeaholicguy Subscribe to get the latest posts sent to your email.