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Arthur
Arthur

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Build Your First MCP Tool in 2026: A Developer Skill Worth Learning

#ai #machinelearning #python #webdev

I am an Arthur, and in this blog I want to talk about a developer skill that I think will become more useful in 2026: building tools for AI agents with Model Context Protocol (MCP).

If you are a developer, you have probably noticed that AI coding tools are changing the way we write software.

AI can already generate functions, fix bugs, explain code, write tests, and work with large codebases. But there is one important step beyond simply asking AI to write code.

That step is giving AI access to your own tools, APIs, databases, files, and workflows.

This is where MCP becomes interesting.

What Is MCP?

MCP stands for Model Context Protocol.

In simple words, MCP gives AI applications a standard way to communicate with external tools and data.

Think about it like this:

AI Assistant
     |
     v
   MCP
     |
     +------ Database
     |
     +------ API
     |
     +------ File System
     |
     +------ Internal Tools
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Without a standard protocol, every AI application may need a different integration.

With MCP, a developer can create a tool that exposes a specific capability in a structured way.

For example, you could create an MCP tool that:

  • searches your database
  • checks server status
  • reads project documentation
  • creates a GitHub issue
  • checks website uptime
  • searches company data
  • runs a controlled internal operation

The official MCP project has continued to expand in 2026, including a new specification with a stateless protocol core, Tasks, improved authorization, and other changes aimed at production use.

Why Should Developers Learn MCP in 2026?

AI coding agents are becoming a normal part of software development.

A 2026 JetBrains developer survey found that 90% of professional developers surveyed were using AI coding agents at work at least weekly, while 68% were using them daily.

This does not mean developers will stop coding.

It means the job is slowly changing.

Instead of only writing code manually, developers increasingly need to understand:

  • how AI agents work
  • how tools are exposed to agents
  • how APIs connect to AI
  • how to validate AI-generated actions
  • how to secure agent access
  • how to build reliable developer workflows

This is why MCP development is a useful skill to explore.

MCP Is Not Just Another AI Prompt

One mistake I see developers make is thinking that AI development is mainly about writing better prompts.

Prompts are useful, but production systems need more.

Imagine you have an internal API:

def get_server_status(server_id):
    return {
        "server": server_id,
        "status": "running",
        "cpu": 42
    }
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A normal application can call this function.

But what if an AI agent could safely discover and use this capability when needed?

That is where a tool-based architecture becomes useful.

The AI does not need to magically know your server information.

You give it a controlled tool.

User
 |
 | "Is server-102 healthy?"
 v
AI Agent
 |
 | calls tool
 v
get_server_status()
 |
 v
Your API / Database
 |
 v
Result
 |
 v
AI Agent
 |
 v
Human-readable answer
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This is much more useful than simply putting everything inside a prompt.

Your First MCP Tool

Let's look at a small example.

Python is a good language for learning this because the syntax is simple.

A basic MCP server can expose a tool that returns information.

For example:

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("Developer Tools")


@mcp.tool()
def get_server_status(server_id: str) -> str:
    """
    Return the current status of a server.
    """

    # Example data
    servers = {
        "server-101": "running",
        "server-102": "stopped",
        "server-103": "maintenance"
    }

    status = servers.get(server_id, "unknown")

    return f"{server_id} status: {status}"


if __name__ == "__main__":
    mcp.run()
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The interesting part is this:

@mcp.tool()
def get_server_status(server_id: str) -> str:
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We are telling the MCP server that this function is a tool.

Now an MCP-compatible client can discover the tool and potentially call it.

Add a Database Tool

Let's make the example a little more realistic.

Suppose you have a SQLite database containing users.

A simple database function could look like this:

import sqlite3


def find_user(email):
    connection = sqlite3.connect("users.db")

    cursor = connection.cursor()

    cursor.execute(
        "SELECT id, name, email FROM users WHERE email = ?",
        (email,)
    )

    user = cursor.fetchone()

    connection.close()

    return user
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We can expose a safe version of this operation as an MCP tool:

@mcp.tool()
def find_user_by_email(email: str) -> str:

    user = find_user(email)

    if not user:
        return "User not found"

    user_id, name, user_email = user

    return (
        f"ID: {user_id}\n"
        f"Name: {name}\n"
        f"Email: {user_email}"
    )
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Developers can also use MCP with existing APIs and infrastructure services such as seimaxim when building AI-powered developer workflows.
Now the AI agent has access to a specific database operation.

Notice something important here.

We are not giving the AI unlimited access to the database.

We are exposing one controlled function.

That difference matters.

Read-Only Tools Are a Good Starting Point

If you are learning MCP development, I recommend starting with read-only tools.

For example:

@mcp.tool()
def get_project_version() -> str:
    return "Project version: 2.4.1"
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Or:

@mcp.tool()
def check_website_status(url: str) -> str:
    return "Website is online"
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Or:

@mcp.tool()
def search_documentation(query: str) -> str:
    return f"Searching documentation for: {query}"
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These tools are easier to understand and safer to test.

After that, you can move toward tools that perform actions.

The Important Part: Tool Design

Creating an MCP tool is easy.

Creating a good MCP tool is harder.

A good tool should have:

  1. A clear name
  2. A simple description
  3. Well-defined parameters
  4. Predictable output
  5. Proper error handling
  6. Security checks
  7. Limited permissions

For example, avoid a vague tool like:

@mcp.tool()
def do_everything(data):
    ...
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Instead, create focused tools:

@mcp.tool()
def get_user(email: str):
    ...


@mcp.tool()
def get_order(order_id: str):
    ...


@mcp.tool()
def check_payment(order_id: str):
    ...
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Small tools are easier for both developers and AI agents to understand.

Tool Descriptions Matter

A tool description works almost like documentation for the AI.

Compare these:

@mcp.tool()
def search(q):
    ...
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with:

@mcp.tool()
def search_documentation(query: str):
    """
    Search internal developer documentation.

    Use this tool when the user asks about
    project configuration, APIs, or internal guides.
    """
    ...
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The second version gives the agent much better information.

This is one of the skills developers should learn: designing tools that are easy for AI agents to understand.

Security Should Come Before Automation

This is probably the most important part of the whole article.

Giving an AI agent access to tools also means giving it the ability to perform actions.

A read-only tool may be relatively low risk.

A tool like this is different:

@mcp.tool()
def delete_user(user_id: str):
    ...
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You should not automatically allow an AI agent to perform destructive operations.

Use authentication, authorization, validation, logging, and human approval where necessary.

For example:

def delete_user(user_id, current_user):

    if not current_user.is_admin:
        raise PermissionError("Admin access required")

    # Delete only after authorization
    ...
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The MCP ecosystem is also putting more attention on authorization and tool risk as these systems move toward production.

MCP + APIs Can Be Very Powerful

One of my favorite use cases is connecting MCP tools to existing APIs.

You don't need to rebuild your entire application.

Imagine your company already has:

REST API
   |
   +--- Users
   +--- Orders
   +--- Billing
   +--- Servers
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You can build an MCP layer on top:

AI Agent
    |
    v
MCP Server
    |
    +--- Users API
    +--- Orders API
    +--- Billing API
    +--- Server API
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Now the AI agent can work with existing systems through controlled tools.

This is especially interesting for internal developer tools and automation.

A Simple Project You Can Build

If you want to practice MCP, don't start with a huge AI project.

Build something small.

Here is one idea:

Developer Server Monitor

Create an MCP server with these tools:

get_server_status()
get_cpu_usage()
get_memory_usage()
get_disk_usage()
get_server_logs()
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Then connect it to your own test environment.

You could ask an AI client:

"Check server-101."

"How much memory is being used?"

"Show me the latest error."

"Is the server healthy?"
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The AI can use the tools and turn their results into normal language.

This small project teaches several useful skills at once:

  • Python
  • APIs
  • MCP
  • JSON
  • authentication
  • error handling
  • logging
  • AI agents
  • tool design

MCP Is Also Useful for Developer Workflows

MCP is not limited to servers.

You can build tools around your development workflow.

For example:

GitHub
   |
   v
MCP Tool
   |
   +--- Search issues
   +--- Read pull requests
   +--- Find repositories
   +--- Check CI status
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Or:

Documentation
      |
      v
   MCP Tool
      |
      v
   AI Agent
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This allows developers to build AI-assisted workflows around systems they already use.

What Skill Should Developers Learn Along With MCP?

MCP itself is only one part of the picture.

If you want to become good at building AI-enabled developer tools, focus on these skills:

1. API Design

Learn how REST APIs work.

Understand:

GET
POST
PUT
PATCH
DELETE
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Also learn authentication, status codes, JSON, rate limits, and error handling.

2. Python or TypeScript

You don't need ten programming languages.

Be very comfortable with one.

Python is a great starting point.

TypeScript is also useful if your projects are heavily JavaScript-based.

3. Git

AI agents can generate code quickly, but you still need to understand:

git diff
git status
git add
git commit
git checkout
git revert
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A developer who cannot review changes is going to have problems with AI-generated code.

4. Testing

Don't trust generated code just because it looks correct.

Write tests.

For example:

def test_server_status():

    result = get_server_status("server-101")

    assert "running" in result
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Testing becomes even more important when AI agents are allowed to perform actions.

5. Security

Learn the basics of:

  • authentication
  • authorization
  • secrets management
  • input validation
  • SQL injection
  • API security
  • least-privilege access

AI makes development faster.

Security mistakes can also become faster.

The Developer's Role Is Changing

In my opinion, the biggest change is not that AI writes code.

The bigger change is that developers are becoming system designers.

Instead of thinking only:

"How do I write this function?"

You may increasingly think:

"What should the agent be allowed to do?"

And:

"Which tools should I expose?"

And:

"How do I verify the result?"

And:

"What happens if the agent makes a wrong decision?"

That is a different type of engineering.

Gartner's 2026 research on emerging skills for agentic coding also points toward deeper systems-engineering skills as AI coding agents move from assistants toward collaborators.

A Simple Learning Roadmap

If MCP is completely new to you, follow this order.

Week 1 — Learn APIs

Build a small REST API.

Learn:

HTTP
JSON
GET
POST
Authentication
Errors
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Week 2 — Build Python Tools

Create simple functions:

get_weather()
get_user()
search_database()
check_server()
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Week 3 — Learn MCP

Turn your functions into MCP tools.

Start with read-only operations.

Week 4 — Build One Real Project

For example:

MCP Developer Assistant
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with tools for:

Search documentation
Check server status
Read logs
Search GitHub issues
Check API health
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This gives you something practical to show in your GitHub portfolio.

Final Thoughts

I don't think developers need to become AI researchers to work with AI in 2026.

But developers should understand how modern AI systems connect to real software.

MCP is one useful piece of that puzzle.

The important skill is not just knowing how to create an MCP server.

It is knowing how to design useful tools, connect them to existing systems, protect them with proper permissions, test their output, and build workflows that developers can actually trust.

AI can write a lot of code.

But someone still needs to decide what the system should be allowed to do.

That is where good developers still matter.

If you are looking for a practical developer skill to learn in 2026, I would put MCP, AI agent integration, API design, testing, and security on the list.

Start with one small tool.

Make it work.

Then make it safe.

Then build something useful with it.

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