AI agents, MCP and skills: what's the difference?
Models, agents, tools, MCP and skills sound interchangeable. Here's how they fit together and what developers actually need to build useful AI software.
AI agents are everywhere, but the terminology around them is getting confusing. Models, agents, tools, MCP servers and skills are often mentioned together, sometimes as though they're interchangeable.
They're not. Each plays a different role, and understanding the differences makes it easier to build useful AI applications.
Start with the model
A model is the intelligence behind an AI application. It interprets instructions, processes information and generates responses.
On its own, though, a model doesn't necessarily have access to your files, databases or applications. It can explain how to update a GitHub repository, but that doesn't mean it can actually update one.
For that, it needs tools.
Tools give AI the ability to act
A tool is a specific capability exposed to an AI system.
It might search the web, read a file, query a database or create a GitHub pull request.
The model can decide when a tool would be useful, while the surrounding application handles execution and returns the result. OpenAI's tools documentation describes this distinction.
Tools don't have to be complicated. A function that retrieves the weather is a tool. So is an API endpoint that creates a calendar event.
An agent brings everything together
An agent is a system that uses a model, tools and an execution loop to work towards a goal.
Rather than simply answering a question, an agent can take several steps, inspect the results and decide what to do next.
Imagine asking an AI assistant to research a technology story and publish an article. An agent might search for announcements, open sources, check for duplicates, write a Markdown file and create a GitHub pull request.
The important difference isn't that the AI is somehow more intelligent. It's that the application allows it to carry out a sequence of actions. OpenAI's Agents SDK supports this kind of multi-step work.
Not every application needs an agent. If a single model request can reliably complete the task, adding an agent loop may introduce unnecessary cost and complexity.
Where MCP fits in
The Model Context Protocol (MCP) is an open standard for connecting AI applications to external tools and information.
Without MCP, developers often build custom integrations between each AI application and the services it needs. MCP provides a common interface through which an application can discover and use capabilities exposed by an MCP server.
For example, a GitHub MCP server might expose tools for reading repository files, creating branches or opening pull requests. The MCP specification defines how servers expose callable tools.
MCP doesn't make the AI an agent, and it doesn't replace underlying APIs. It standardises how capabilities are made available to AI applications. Connecting a server also doesn't mean every action should be trusted or automatically approved.
Skills tell an agent how to work
If tools describe what an agent can do, skills help describe how it should approach a task.
A skill can package instructions, conventions, scripts and supporting resources into a reusable workflow. The open Agent Skills format uses folders containing instructions and optional supporting files.
Consider an assistant that writes articles for a technology website. Its tools allow it to search the web and commit files to GitHub. Its writing skill might specify British English, a word limit, source requirements and a Markdown format.
The skill helps the agent use its capabilities consistently. Exact implementations vary across AI platforms.
How the pieces fit together
| Component | Responsibility |
|---|---|
| Model | Understands the request and generates content |
| Agent | Coordinates research and publishing |
| Tools | Search the web, read files and update GitHub |
| MCP | Provides standard connections to external capabilities |
| Skills | Define editorial rules and workflow conventions |
You don't always need all five.
Use direct API integrations for a handful of predictable operations. Consider MCP when integrations should be reusable across AI clients. Introduce skills when workflows need consistent instructions. Use an agent when work genuinely requires multiple decisions and feedback from earlier steps.
More agentic doesn't automatically mean better. A reliable AI application is defined by whether it completes useful work accurately, safely and consistently—not how many frameworks it uses.