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Yogitaadevi Ravishankar
Yogitaadevi Ravishankar

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Building My First MCP Server with Claude and Python

#mcp #claude #ai #python

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
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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
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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:

  1. Refine the content
  2. 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:

  1. Read the markdown file
  2. Called the Dev.to API
  3. 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"
}
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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!

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