DEV Community: Yogitaadevi Ravishankar

Building My First MCP Server with Claude and Python

Yogitaadevi Ravishankar — Tue, 26 May 2026 17:19:24 +0000

Building My First MCP Server with Claude and Python

A few days ago, I started exploring MCP (Model Context Protocol) and wanted to understand how AI tools actually interact with external systems. Instead of just reading documentation, I decided to build a simple, real-world project:

A custom MCP server that allows Claude to publish blog posts directly to the Dev.to platform.

This project helped me understand MCP servers, AI tool calling, Claude integrations, agent workflows, and structured AI automation. In this post, I'll share what I built, how it works, and what I learned along the way.

What is MCP?

MCP (Model Context Protocol) is a protocol that allows AI models like Claude to interact with external tools and systems securely.

In simple terms — MCP gives AI the ability to do things, not just answer questions. With MCP, an AI can:

Read and create files
Call external APIs
Publish content
Access databases
Interact with applications

This is one of the key foundations behind AI agents and agentic workflows.

What I Built

I created a simple MCP server using:

Python — for building the MCP tools
Claude Desktop — as the AI interface
Dev.to API — for publishing blog posts

The end-to-end workflow looked like this:

Text File → Claude Refinement → Markdown Generation → Dev.to Publishing

Project Flow

Here's how the system worked, step by step.

Step 1 — Configure Claude Desktop

I edited the Claude Desktop configuration file and connected my custom MCP server. After restarting Claude Desktop, the custom tool became available inside Claude.

This was the moment it clicked for me — seeing how MCP tools integrate directly with an AI system.

Step 2 — Create MCP Tools

Using Python, I created tools that could:

Read blog content from a file
Generate markdown output
Publish blogs to Dev.to via API

The basic structure looked like this:

@app.tool()
def publish_blog():
    pass

This registers the function as a tool that Claude can discover and invoke.

Step 3 — Provide Raw Blog Content

I created a plain text file containing unrefined blog content, then asked Claude to:

Refine the content
Convert it into proper markdown format

Claude successfully generated the polished markdown file — ready for publishing.

Step 4 — Publish to Dev.to

Finally, I asked Claude to use the MCP tool to publish the markdown file to Dev.to. The workflow:

Read the markdown file
Called the Dev.to API
Published the article automatically

Seeing an AI system interact with an external API through a tool I built was genuinely exciting.

What I Learned

This project taught me far more than just API integration.

1. AI Tools Need Structured Outputs

AI systems work much better with predictable, structured responses. Instead of returning a plain string like "Success", it's far better to return:

{
  "success": true,
  "message": "Blog published successfully"
}

Structured outputs make tools easier for AI agents to parse and act on reliably.

2. Error Handling is Critical

AI agents can fail in unexpected ways, so tools must handle edge cases gracefully — including invalid inputs, API failures, missing files, and network errors. Robust error handling is what separates a toy from a reliable system.

3. MCP Reframes How We Think About APIs

Traditional APIs are designed for frontend apps and human users. MCP tools, on the other hand, are designed for AI systems. That shift means clear descriptions, structured schemas, predictable outputs, and machine-readable errors become critically important.

4. AI + Tools Feels Fundamentally Different

This project made the difference between a chatbot and an AI agent tangible for me. A chatbot answering questions is one thing. An AI that reads files, refines content, generates markdown, and publishes blogs autonomously is something else entirely — and far more powerful.

Challenges I Faced

No project is without its rough edges. Some issues I ran into:

Package installation conflicts
uv environment setup
Mistakes in the Claude Desktop config
MCP tool detection issues
API debugging

Solving each of these helped me understand the ecosystem at a much deeper level than just following a tutorial would have.

Technologies Used

Tool	Purpose
Python	Building MCP tools
Claude Desktop	AI interface
MCP	AI-to-tool communication protocol
Dev.to API	Blog publishing
uv	Python environment management
Markdown	Content format

Final Thoughts

Building this MCP server completely changed how I think about AI systems. We are moving from AI that only responds to AI systems that can act — using tools to read, write, create, and publish autonomously.

This was a small project, but it gave me a strong, practical introduction to the future of AI tooling. If you're curious about AI agents and agentic workflows, I highly recommend building something like this yourself. The best way to understand it is to build it.

Have questions or want to share your own MCP experiments? Drop a comment below!

AI vs Agentic AI: They Sound Similar, But They're Not

Yogitaadevi Ravishankar — Tue, 26 May 2026 04:43:04 +0000

AI vs Agentic AI: They Sound Similar, But They're Not

When I first started learning about AI systems, I thought terms like AI, AI Agents, and Agentic AI all meant the same thing. But after exploring MCP servers, tool calling, and agent workflows, I realized there's a fundamental difference between them.

In this post, I'll break down each concept in a simple, practical way — with real-world examples any developer can relate to.

What is AI?

AI (Artificial Intelligence) is the broad concept of machines performing tasks that normally require human intelligence.

Everyday examples:

Netflix recommendations
Spam email detection
Face unlock on your phone
ChatGPT answering questions

AI is great at:

Recognizing patterns
Understanding and processing data
Generating responses
Making predictions

But most traditional AI systems are reactive — they respond to input. They don't independently plan or execute complex, multi-step goals on their own.

What is an AI Agent?

An AI Agent is an AI system that can use tools to perform tasks beyond just answering questions.

Instead of only generating a response, it can:

Read and write files
Call external APIs
Search the web
Send emails
Query databases
Execute workflows

Think of it this way:

AI = A smart brain

AI Agent = A smart brain with hands

Simple Example

If you ask a traditional AI:

"What is the weather in Chennai?"

It gives you an answer.

But if you ask an AI Agent:

"Book me a cab if it's raining outside."

The agent can:

Check the current weather
Decide whether it's raining
Call a cab-booking tool
Confirm the booking

This is possible because the agent can interact with external systems — not just generate text.

What is Agentic AI?

This is where things get significantly more advanced.

Agentic AI is not just about using tools. It's about:

Planning a path to achieve a goal
Reasoning through complex problems
Making decisions dynamically at each step
Retrying after failures automatically
Working autonomously toward a larger objective — without needing step-by-step instructions

Instead of waiting for you to guide every move, an agentic system understands the final objective and figures out the process itself.

Real-World Analogy

System	Analogy
Traditional AI	A smart student answering exam questions
AI Agent	An assistant employee following instructions and using tools
Agentic AI	A project manager who understands the goal, breaks it into tasks, checks progress, fixes failures, and delivers the outcome

A Developer's Perspective

As a React/Next.js developer, this distinction became very clear through practical examples.

Traditional AI

You ask:

"Generate a login component."

The AI generates the code. Done.

AI Agent

You ask:

"Create a login flow."

The agent may:

Generate the UI component
Create the API call logic
Add form validation
Use relevant files or tools

But it still largely follows your instructions step by step.

Agentic AI

You ask:

"Build authentication for my SaaS app."

An agentic system may:

Choose the right authentication strategy (JWT, OAuth, etc.)
Break the work into frontend and backend tasks
Generate and configure APIs
Debug errors autonomously
Retry failed operations
Update files across the codebase
Validate the entire flow end-to-end
Continue until the goal is fully achieved

This is a completely different level of autonomy.

The Core Difference at a Glance

System	Behavior
AI	Responds to prompts
AI Agent	Performs tasks using tools
Agentic AI	Independently plans and completes goals

Why This Matters

We are gradually moving from:

Question-Answering AI → Goal-Solving AI

That shift is enormous. Future AI systems won't just answer "What should I do?" — they will increasingly:

Decide
Plan
Execute
Recover from failures
Coordinate across tools
Complete objectives end-to-end

How Building MCP Servers Changed My Perspective

While experimenting with MCP (Model Context Protocol) servers and AI tools, I noticed something important:

Traditional APIs are designed for humans. But agent systems need:

Structured, predictable outputs
Clear schemas
Reliable error handling
Consistent response formats

Because agents cannot guess the way humans do. That's why concepts like:

Schema validation
Structured JSON responses
Tool metadata
Retry logic
Planning loops

...become critically important when building for agentic systems.

Final Thoughts

AI is evolving at a remarkable pace. Understanding the difference between AI, AI Agents, and Agentic AI helps developers better prepare for the next generation of software.

We are no longer building applications only for users.

We are starting to build systems that AI itself can use.

And that changes everything.

I'm currently learning MCP servers, AI agents, and agentic systems as a frontend developer transitioning into AI engineering. Follow along if you're exploring this space too!