An intelligent Dev.to assistant powered by the OpenAI Agents SDK, Model Context Protocol (MCP), Chainlit, and OpenRouter LLMs.

This project demonstrates how AI Agents can dynamically discover and execute tools exposed through a custom MCP Server, enabling natural language interactions with Dev.to. Users can search articles, explore trending content, retrieve author-specific posts, and even generate and publish new blog articles—all through a conversational interface.

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📖 Overview

The Dev.to AI Agent acts as an intelligent bridge between users and the Dev.to platform.

Instead of hardcoding API calls directly into the application, all Dev.to operations are exposed as MCP tools through a custom MCP Server. The AI Agent analyzes user intent, discovers available tools, selects the appropriate tool, executes it, and returns a summarized response.

Example Use Cases

• Browse latest Dev.to articles
• Discover trending content
• Search articles by keyword
• Find articles by tag
• Explore articles by author
• Retrieve article details
• Generate and publish new blog posts

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✨ Features

• 🤖 AI-powered Dev.to assistant
• 🔌 MCP-based tool architecture
• 📰 Browse latest Dev.to articles
• 🔥 Discover trending articles
• 🏷️ Search articles by tags
• 🔍 Keyword-based article search
• 👤 Find articles by author
• ✍️ AI-generated article creation
• ⚡ Fully asynchronous architecture
• 💬 Modern conversational UI with Chainlit
• 🧠 Intelligent tool selection using OpenAI Agents SDK
• 🚀 Easily extensible MCP tool ecosystem

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🏗️ Architecture

┌──────────────────────────┐
│      Chainlit UI         │
│       (app.py)           │
└────────────┬─────────────┘
             │
             ▼
┌──────────────────────────┐
│   OpenAI Agents SDK      │
│     (mcp_agent.py)       │
└────────────┬─────────────┘
             │
             ▼
┌──────────────────────────┐
│    Dev.to AI Agent       │
│  Instruction Driven      │
└────────────┬─────────────┘
             │
             ▼
┌──────────────────────────┐
│   MCP Server (STDIO)     │
│      (server.py)         │
└────────────┬─────────────┘
             │
             ▼
┌──────────────────────────┐
│      MCP Tools           │
└────────────┬─────────────┘
             │
             ▼
┌──────────────────────────┐
│      Dev.to APIs         │
└──────────────────────────┘

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🔄 End-to-End Flow

User
 │
 ▼
Chainlit Chat Interface
 │
 ▼
OpenAI Agent SDK
 │
 ▼
Dev.to AI Agent
 │
 ▼
MCP Tool Discovery
 │
 ▼
Tool Selection
 │
 ▼
MCP Tool Execution
 │
 ▼
Dev.to API
 │
 ▼
Agent Summarization
 │
 ▼
Response Displayed in UI

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💬 User Experience

The application uses Chainlit to provide a modern conversational interface.

Starter Prompts

Users can quickly explore the agent using built-in starter suggestions:

• 📰 Latest Articles
• 🔥 Top Trending Articles
• 🐍 Python Articles
• 🤖 AI Articles
• 🔍 Search Articles
• ✍️ Generate New Article

These prompts help users immediately understand the capabilities of the agent.

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⚙️ Agent Execution Lifecycle

Every request follows the same execution workflow:

1. User sends a message

2. Chainlit receives the request

3. Thinking indicator is displayed

4. AI Agent is invoked

5. MCP Server starts

6. Agent discovers available tools

7. Agent selects the appropriate tool

8. MCP Tool executes

9. Dev.to API returns data

10. Agent summarizes results

11. Response is displayed to the user

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🛠️ Technology Stack

Technology	Purpose
Python	Core programming language
Chainlit	Conversational AI interface
OpenAI Agents SDK	Agent orchestration and tool calling
MCP (Model Context Protocol)	Tool discovery and execution
OpenRouter	LLM provider
Qwen 3 235B	Foundation language model
AsyncIO	Asynchronous execution
MCPServerStdio	MCP communication layer
dotenv	Environment variable management

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📂 Project Structure

project/
│
├── app.py
├── mcp_agent.py
├── server.py
├── .env
├── requirements.txt
└── README.md

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📄 File Breakdown

app.py

This file contains the Chainlit user interface and handles all user interactions.

Responsibilities

• Displays the chat interface
• Registers starter prompts
• Receives user messages
• Displays agent progress
• Shows final responses

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set_starters()

Registers predefined starter prompts displayed on an empty chat screen.

Purpose

• Improve user onboarding
• Demonstrate available capabilities
• Provide quick actions

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on_chat_start()

Triggered whenever a new conversation begins.

Responsibilities

• Displays welcome message
• Introduces the assistant
• Explains supported actions

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on_message()

Main message processing function.

Responsibilities

• Receives user input
• Displays thinking indicator
• Invokes AI agent
• Displays final response

Flow

User Message
      │
      ▼
on_message()
      │
      ▼
agent_main()
      │
      ▼
Agent Execution
      │
      ▼
Tool Execution
      │
      ▼
Response Returned
      │
      ▼
UI Updated

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mcp_agent.py

This file contains the AI Agent orchestration logic.

It acts as the bridge between:

• User Queries
• Language Model
• MCP Server
• MCP Tools

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setup_dev_agent()

Creates and configures the Dev.to Agent.

Responsibilities

• Defines agent instructions
• Connects MCP Server
• Attaches language model
• Configures tool usage behavior

The agent receives detailed instructions describing:

• Which tool should be used
• When it should be used
• How results should be formatted

This improves tool-calling reliability and response quality.

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main()

Primary execution entry point for all requests.

Responsibilities

• Starts MCP Server
• Creates agent instance
• Executes agent workflow
• Returns final output

Flow

User Query
    │
    ▼
Start MCP Server
    │
    ▼
Create Agent
    │
    ▼
Run Agent
    │
    ▼
Tool Execution
    │
    ▼
Final Response

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server.py

This file contains the MCP Server implementation.

The server exposes Dev.to functionality as MCP-compatible tools that can be dynamically discovered and used by the AI Agent.

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🔌 MCP Tools

The MCP Server exposes multiple tools to the agent.

get_latest_articles()

Returns the latest articles from Dev.to.

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get_top_articles()

Returns trending or top-performing articles.

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get_articles_by_tag(tag)

Retrieves articles belonging to a specific tag.

Example

Python
JavaScript
AI
MachineLearning
Agents

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search_articles(query)

Performs keyword-based article search.

Example

LangChain
Agentic AI
OpenAI
MCP

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get_articles_by_username(username)

Returns articles published by a specific author.

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get_article_details(article_id)

Retrieves detailed information for a specific article.

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create_article()

Creates and publishes a new Dev.to article.

Parameters

title
body_markdown
tags
published

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🤖 Intelligent Tool Selection

The AI Agent uses instruction-based reasoning to select the correct MCP tool.

Example 1

User:

Show me the latest articles

Agent:

Calls get_latest_articles()

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Example 2

User:

Search for Agentic AI

Agent:

Calls search_articles("Agentic AI")

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Example 3

User:

Show articles by ThePracticalDev

Agent:

Calls get_articles_by_username()

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Example 4

User:

Create an article about MCP Servers

Agent:

Generates content
      ↓
Calls create_article()

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🔌 Why MCP?

This project intentionally uses Model Context Protocol (MCP) rather than directly integrating APIs into the agent.

Benefits

• Standardized tool interface
• Dynamic tool discovery
• Decoupled architecture
• Easier maintenance
• Reusable integrations
• Future multi-agent compatibility
• Scalable tool ecosystem

New tools can be added to the MCP Server without changing the core agent implementation.

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📸 Screenshots

Home Screen

Add screenshot here

Agent Execution

Add screenshot here

Article Generation

Add screenshot here

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🚀 Example Queries

Show me the latest Dev.to articles

Show me the top trending articles

Find articles about Python

Find articles about AI agents

Search for machine learning articles

Show articles by ThePracticalDev

Create a blog about the latest advancements in Agentic AI

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🔮 Future Enhancements

• Multi-agent workflows
• Medium integration
• Hashnode integration
• Memory-enabled agents
• Blog review agent
• SEO optimization agent
• Research assistant agent
• Content planning agent
• Analytics dashboard
• Streaming responses
• Human approval workflows

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🎯 Learning Outcomes

This project demonstrates practical implementation of:

• OpenAI Agents SDK
• MCP (Model Context Protocol)
• Tool Calling Agents
• Agent Instructions Engineering
• Async Python Development
• OpenRouter Integration
• Chainlit Applications
• AI-Powered Content Automation
• Agentic AI Workflows

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🚀 Getting Started

Clone the Repository

git clone https://github.com/Dharshini195/Blog-MCP.git
cd Blog-MCP

Install Dependencies

pip install -r requirements.txt

Configure Environment Variables

Create a .env file:

OPENROUTER_API_KEY=your_openrouter_api_key

Run the Application

chainlit run app.py

Open the provided local URL and start chatting with the Dev.to Assistant.

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📜 License

This project is intended for educational, experimentation, and learning purposes. Feel free to fork, modify, and extend it for your own MCP and Agent Engineering projects.