Building an AI-Powered Deep Web Search Blog Generator with mcp-use: A Complete Guide

16 min readJul 14, 2025

How to create an intelligent web application that automatically generates engaging blog posts using advanced AI models and MCP servers

Introduction

In the rapidly evolving landscape of AI and web development, the Model Context Protocol (MCP) has emerged as a powerful framework for building intelligent applications that can seamlessly integrate multiple AI services and tools. This article explores the development of an AI-powered blog generator that leverages MCP to perform deep web searches, generate content, and automatically save files.

Working with Multiple MCP Servers 🪐🪐

One MCP server is great, but real-world workflows often need several specialised back-ends — think scraping the web, querying a database, and editing files, all in one agent. Good news: MCP-Use was built for exactly this.

What is mcp_use ?

It is a open source MCP client library. It facilitates to connect any LLM to any MCP server.

The project demonstrates how to build a production-ready web application that combines:

Web search capabilities using Linkup MCP server
AI-powered content generation using DeepSeek models via Groq API
File management using MCP filesystem server
Modern web interface with FastAPI and responsive design

Project Overview

The MCP Blog Generator is a web application that allows users to:

1. Input a blog topic through an intuitive web interface

2. Automatically search the web for relevant information using MCP Linkup server

3. Generate engaging blog posts using AI models with emojis and interesting content

4. Save blog posts automatically to a local filestore using MCP filesystem server

5. Download and share generated content

Key Features

• 🔍 Intelligent Web Search: Real-time search using Linkup MCP server

• 🤖 AI Content Generation: High-quality blog posts using DeepSeek-r1-distill-llama-70b

• 💾 Automatic File Management: Seamless saving using MCP filesystem server

• 📱 Responsive Web Interface: Modern UI with DaisyUI components

• ⚡ Fast Performance: Built with FastAPI for high-speed operations

• 🔄 Workflow Automation: Complete end-to-end blog generation process

Technology Stack

Backend Framework

• FastAPI: Modern, fast web framework for building APIs with Python

• Uvicorn: ASGI server for running FastAPI applications

• Jinja2: Template engine for dynamic HTML generation

AI and MCP Integration

• MCP (Model Context Protocol): Framework for connecting AI models with tools and data sources

• mcp-use: Python library for MCP client and agent management

• LangChain: Framework for building applications with LLMs

• Groq API: High-performance inference platform for AI models

AI Models

DeepSeek-r1-distill-llama-70b: Advanced language model for content generation
OpenAI-4O-mini : Agent Brain

• Linkup MCP Server: Web search capabilities

• Filesystem MCP Server: File management operations

Frontend and Styling

• DaisyUI: Component library for Tailwind CSS

• Tailwind CSS: Utility-first CSS framework

• Font Awesome: Icon library

• Responsive Design: Mobile-first approach

Development Tools

• Python 3.12: Programming language

• pip/uv: Package management

• Git: Version control

• Windows 10/11: Development environment

Architecture and Workflow

System Architecture

Workflow Process

1. User Input: User enters a blog topic through the web interface

2. Web Search: MCP agent uses Linkup server to search for relevant information

3. Search Results Display: Results are shown to user for review

4. Content Generation: AI model generates engaging blog post based on search results

5. File Saving: Blog post is automatically saved to filestore using MCP filesystem server

6. Results Display: User can view, download, and share the generated blog post

Installation and Setup

Prerequisites

• Python 3.12 or higher

• Node.js and npm (for MCP servers)

• UV package manager (for Python dependencies)

• Groq API key

• Linkup API key

Step 1: Clone and Setup Project

cd mcpuse_dir

# Create virtual environment
python -m venv my_env
my_env\Scripts\activate # Windows
# source my_env/bin/activate # Linux/Mac

Step 2: Install Python Dependencies

# Install required packages
 pip install fastapi uvicorn jinja2 python-dotenv
 pip install mcp-use langchain-groq langchain-openai
 pip install python-multipart
 
 # Or using uv
 uv pip install fastapi uvicorn jinja2 python-dotenv
 uv pip install mcp-use langchain-groq langchain-openai
 uv pip install python-multipart

Step 3: Install MCP Servers

# Install Linkup search server
 uv pip install mcp-search-linkup
 
 # Install filesystem server
 npm install -g @modelcontextprotocol/server-filesystem

Step 4: Configure Environment Variables

Create a .env file in your project root:

GROQ_API_KEY=your_groq_api_key_here
LINKUP_API_KEY=your_linkup_api_key_here

Step 5: Configure MCP Servers

Create multiserver_setup_config.json:

{
  "mcpServers": {
    "linkup": {
      "command": "uvx",
      "args": ["mcp-search-linkup"],
      "env": {
        "LINKUP_API_KEY": "Your Linkup API Key"
      }
    },
    "filesystem": {
      "command": "C:\\Program Files\\nodejs\\npx.cmd",
      "args": [
        "@modelcontextprotocol/server-filesystem",
        "./filestore"
      ]
    }
  }
}

Step 6: Create Directory Structure

mkdir filestore
 mkdir static
 mkdir templates

Step 7: Run the Application

python app.py

The application will be available at http://127.0.0.1:8000

Response Logs

(my_env) C:\Users\nayak\Documents\mcpuse_dir>python app.py
INFO:     Started server process [37032]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO:     127.0.0.1:60206 - "GET / HTTP/1.1" 200 OK
2025-07-13 22:20:01,111 - mcp_use.telemetry.telemetry - INFO - Anonymized telemetry enabled. Set MCP_USE_ANONYMIZED_TELEMETRY=false to disable.
🔍 Performing web search for: TOP AI News in 2025
2025-07-13 22:20:01,115 - mcp_use - INFO - 🚀 Initializing MCP agent and connecting to services...
2025-07-13 22:20:01,116 - mcp_use - INFO - 🔌 Found 0 existing sessions
2025-07-13 22:20:01,117 - mcp_use - INFO - 🔄 No active sessions found, creating new ones...
2025-07-13 22:20:15,464 - mcp_use - INFO - ✅ Created 2 new sessions
2025-07-13 22:20:15,743 - mcp_use - INFO - 🛠️ Created 13 LangChain tools from client
2025-07-13 22:20:15,744 - mcp_use - INFO - 🧰 Found 13 tools across all connectors
2025-07-13 22:20:15,746 - mcp_use - INFO - 🧠 Agent ready with tools: search-web, read_file, read_multiple_files, write_file, edit_file, create_directory, list_directory, list_directory_with_sizes, directory_tree, move_file, search_files, get_file_info, list_allowed_directories
2025-07-13 22:20:15,970 - mcp_use - INFO - ✨ Agent initialization complete
2025-07-13 22:20:15,971 - mcp_use - INFO - 💬 Received query: 'Use the 'linkup' server to search for: TOP AI News...'
2025-07-13 22:20:15,972 - mcp_use - INFO - 🏁 Starting agent execution with max_steps=30
2025-07-13 22:20:15,972 - mcp_use - INFO - 👣 Step 1/30
2025-07-13 22:20:24,500 - mcp_use - INFO - 🔧 Tool call: search-web with input: {'query': 'TOP AI News in 2025', 'depth': 'standard'}
2025-07-13 22:20:24,502 - mcp_use - INFO - 📄 Tool result: results=[LinkupSearchTextResult(type='text', name='Latest AI Breakthroughs and News: May, June, J...
2025-07-13 22:20:24,502 - mcp_use - INFO - 👣 Step 2/30
2025-07-13 22:20:31,758 - mcp_use - INFO - ✅ Agent finished at step 2
2025-07-13 22:20:31,759 - mcp_use - INFO - 🎉 Agent execution complete in 30.64415144920349 seconds
INFO:     127.0.0.1:60207 - "POST /search HTTP/1.1" 200 OK
📝 Generating blog post for: TOP AI News in 2025
💾 Saving blog post to filestore...
2025-07-13 22:20:49,700 - mcp_use - INFO - 🚀 Initializing MCP agent and connecting to services...
2025-07-13 22:20:49,700 - mcp_use - INFO - 🔌 Found 0 existing sessions
2025-07-13 22:20:49,700 - mcp_use - INFO - 🔄 No active sessions found, creating new ones...
2025-07-13 22:21:04,056 - mcp_use - INFO - ✅ Created 2 new sessions
2025-07-13 22:21:04,107 - mcp_use - INFO - 🛠️ Created 13 LangChain tools from client
2025-07-13 22:21:04,107 - mcp_use - INFO - 🧰 Found 13 tools across all connectors
2025-07-13 22:21:04,107 - mcp_use - INFO - 🧠 Agent ready with tools: search-web, read_file, read_multiple_files, write_file, edit_file, create_directory, list_directory, list_directory_with_sizes, directory_tree, move_file, search_files, get_file_info, list_allowed_directories
2025-07-13 22:21:04,233 - mcp_use - INFO - ✨ Agent initialization complete
2025-07-13 22:21:04,233 - mcp_use - INFO - 💬 Received query: 'Use the tool `write_file` from the `filesystem` se...'
2025-07-13 22:21:04,235 - mcp_use - INFO - 🏁 Starting agent execution with max_steps=30
2025-07-13 22:21:04,235 - mcp_use - INFO - 👣 Step 1/30
2025-07-13 22:21:16,897 - mcp_use - INFO - 🔧 Tool call: write_file with input: {'path': 'filestore/blog_top_ai_news_in_2025_20250713_222049.md', 'content': "# Top AI News in 20...
2025-07-13 22:21:16,897 - mcp_use - INFO - 📄 Tool result: Successfully wrote to filestore/blog_top_ai_news_in_2025_20250713_222049.md
2025-07-13 22:21:16,897 - mcp_use - INFO - 👣 Step 2/30
2025-07-13 22:21:18,303 - mcp_use - INFO - ✅ Agent finished at step 2
2025-07-13 22:21:18,303 - mcp_use - INFO - 🎉 Agent execution complete in 28.603514909744263 seconds
INFO:     127.0.0.1:60217 - "POST /generate_blog HTTP/1.1" 200 OK

File Written to filestore directory

Blog Generated

# Top AI News in 2025: The Future Is Here! 🚀🤖

Welcome to the world of Artificial Intelligence in 2025, where innovation is moving at lightning speed, and the implications for society are profound. Buckle up as we explore the most exciting AI news stories that are shaping our future!

## 1. OpenAI Unveils GPT-4.5: The Emotional AI Revolution 💬❤️

On February 28, 2025, OpenAI took a giant leap forward with the launch of GPT-4.5! This cutting-edge model focuses on enhanced emotional intelligence, enabling more human-like conversations. Imagine chatting with an AI that truly understands your feelings and responds with empathy. This breakthrough could revolutionize customer service, therapy, and personal assistants, making our interactions with technology more meaningful than ever. [Read more](https://www.crescendo.ai/news/latest-ai-news-and-updates).

## 2. Mira Murati’s Thinking Machines Lab: A $2 Billion Vision 💡💰

Mira Murati, a visionary in AI, raised an astonishing $2 billion at a $10 billion valuation for her new venture, Thinking Machines Lab. The aim? To develop advanced agentic AI systems that can think and learn autonomously. This ambitious project could redefine the role of AI in decision-making across industries, from finance to healthcare. Get ready for machines that not only assist but also collaborate! [Read more](https://www.crescendo.ai/news/latest-ai-news-and-updates).

## 3. China’s AI Response: A New Era of Independence 🇨🇳⚙️

In a bold move following U.S. trade restrictions, China is accelerating its AI development. With a focus on national independence in technology, new models and collaborations are emerging at a rapid pace. This shift is reshaping the global AI landscape, highlighting the need for nations to bolster their tech ecosystems to remain competitive. [Read more](https://www.crescendo.ai/news/latest-ai-news-and-updates).

## 4. U.S. AI Leadership Concerns: A Call to Action 📈🇺🇸

Blake Moore, a prominent tech advisor, issued a wake-up call regarding potential complacency in U.S. AI leadership. His message is clear: immediate action is needed to maintain a competitive edge against rising powers like China. This call for vigilance emphasizes the importance of innovation and investment to secure the future of AI in America. [Read more](https://www.crescendo.ai/news/latest-ai-news-and-updates).

## 5. Google’s AI Integration: Enhancing Everyday Life 🛍️🔍

Google has been busy integrating AI into various products, from enhanced search features to new shopping experiences. But that's not all! The tech giant is also leveraging AI to improve healthcare and education, aiming to make life easier and more efficient for everyone. With AI as a core component of their strategy, Google is setting the stage for a smarter future! [Read more](https://blog.google/technology/ai/google-ai-updates-june-2025/).

## 6. Stanford’s AI Index Report: Responsible AI Growth 📊🌍

The 2025 AI Index from Stanford highlights the rapid growth of AI models, with increasing emphasis on responsible AI practices. This report sheds light on the economic impacts of AI and the importance of ethical considerations as we advance. As AI continues to permeate our lives, ensuring that it benefits all of humanity is more critical than ever. [Read more](https://hai.stanford.edu/ai-index/2025-ai-index-report).

## 7. AI Startup Boom: The Future is Now! 🌟🚀

The AI startup ecosystem is thriving, with significant investments flowing into generative AI and automation technologies. These innovative companies are pushing the boundaries of what's possible, creating solutions that could transform industries overnight. Keep an eye on this space—it's where tomorrow's breakthroughs are happening today! [Read more](https://www.crn.com/news/ai/2025/the-10-hottest-ai-startup-companies-of-2025-so-far).

## 8. The Global AI Divide: Bridging the Gap 🌐⚖️

A recent report underscores a growing disparity in AI computing power between nations, leading to unequal access to technology and capabilities. As some countries race ahead, others risk being left behind. Addressing this global AI divide is crucial for ensuring equitable access and fostering a balanced technological future. [Read more](https://www.nytimes.com/interactive/2025/06/23/technology/ai-computing-global-divide.html).

---

As we delve deeper into 2025, it's clear that AI is no longer just a futuristic concept—it's an integral part of our daily lives. From emotional conversations with AI companions to the rise of innovative startups, the advancements in this field promise a transformative impact on society. Stay tuned, because the AI revolution is just getting started! 🌟✨

✅ Success Summary:

Both servers connected successfully:

✅ linkup server (using uvx mcp-search-linkup)
✅ filesystem server (using C:\Program Files\nodejs\npx.cmd)

Agent initialization completed:

✅ Created 2 new sessions
✅ Found 13 tools across all connectors
✅ Agent ready with tools: search-web, read_file, write_file, etc.

Full workflow executed successfully:

✅ Step 1: Web search for “Top 5 Agentic Frameworks” completed
✅ Step 2: Blog post generation completed
✅ Step 3: File saved successfully to filestore/Top_5_Agentic_AI_Frameworks.md

Library Details and Usage

Core Libraries

1. MCP (Model Context Protocol)

• Purpose: Framework for connecting AI models with tools and data sources

• Key Features:

– Standardized communication between AI models and tools

– Support for multiple server types (search, filesystem, etc.)

– Extensible architecture for custom servers

• Usage: Enables seamless integration of web search and file operations

2. mcp-use

• Purpose: Python library for MCP client and agent management

• Key Features:

– Simplified MCP client creation and management

– Built-in agent capabilities for tool orchestration

– Session management and connection handling

• Usage: Creates MCP agents that can use multiple servers simultaneously

3. FastAPI

• Purpose: Modern web framework for building APIs

• Key Features:

– High performance with async support

– Automatic API documentation

– Type hints and validation

– Easy integration with frontend frameworks

• Usage: Provides the web server and API endpoints for the blog generator

4. LangChain

• Purpose: Framework for building applications with LLMs

• Key Features:

– Chain and agent abstractions

– Tool integration capabilities

– Memory and state management

– Multiple LLM provider support

• Usage: Orchestrates AI model interactions and tool usage

5. Groq API

• Purpose: High-performance inference platform

• Key Features:

– Ultra-fast model inference

– Support for multiple model types

– Cost-effective pricing

– Reliable API endpoints

• Usage: Powers the DeepSeek model for content generation

Integration Libraries

6. DaisyUI

• Purpose: Component library for Tailwind CSS

• Key Features:

– Pre-built, accessible components

– Consistent design system

– Easy customization

– Responsive design support

• Usage: Provides the UI components for the web interface

7. Jinja2

• Purpose: Template engine for Python

• Key Features:

– Dynamic HTML generation

– Template inheritance

– Variable substitution

– Security features

• Usage: Renders dynamic HTML pages with search results and blog content

Key Implementation Details

MCP Agent Configuration

def get_mcp_agent():
"""Initialize MCP agent with OpenAI model"""
    llm = ChatOpenAI(
        model="gpt-4o-mini",
        temperature=0.7,
        api_key=os.getenv("OPAPIKEY")
    )
 
 client = MCPClient.from_config_file("multiserver_setup_config.json")
 agent = MCPAgent(
 llm=llm,
 client=client,
 use_server_manager=False,
 max_steps=30
 )
 return agent

Web Search Implementation

@app.post("/search")
 async def perform_web_search(request: Request, topic: str = Form(…)):
 agent = get_mcp_agent()
 search_result = await agent.run(
 f"Use the 'linkup' server to search for: {topic}"
 )
 # Return search results to template

Blog Generation Process

@app.post("/generate_blog")
 async def generate_blog_post(request: Request, topic: str = Form(…), search_results: str = Form(…)):
 llm = ChatGroq(
 model="deepseek-r1-distill-llama-70b",
 temperature=0.7,
 api_key=os.getenv("GROQ_API_KEY")
 )
 
 blog_post = await llm.ainvoke(
 f"Write an engaging blog post about the topic: {topic} based on the search results: {search_results} Use emojis and make it interesting."
 )
 
 # Save to filestore using MCP filesystem server
 save_result = await agent.run(
 f"Use the tool `write_file` from the `filesystem` server and write filename: '{filename}' at filestore directory and save content: {blog}"
 )

File Management

The application uses the MCP filesystem server to automatically save generated blog posts with timestamps and descriptive filenames:

timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
 filename = f"blog_{topic.replace(' ', '_').lower()}_{timestamp}.md"

Code Implementation

server setup config files

{
  "mcpServers": {
    "linkup": {
      "command": "uvx",
      "args": ["mcp-search-linkup"],
      "env": {
        "LINKUP_API_KEY": "YOUR API KEY"
      }
    },
    "filesystem": {
      "command": "C:\\Program Files\\nodejs\\npx.cmd",
      "args": [
        "@modelcontextprotocol/server-filesystem",
        "./filestore"
      ]
    }
  }
}

FastAPI application

from fastapi import FastAPI, Request, Form, HTTPException
from fastapi.responses import HTMLResponse, RedirectResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
import asyncio
import os
from datetime import datetime
import json
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langchain_groq import ChatGroq
from mcp_use import MCPAgent, MCPClient

load_dotenv()

app = FastAPI(title="MCP Blog Generator", version="1.0.0")

# Mount static files
app.mount("/static", StaticFiles(directory="static"), name="static")

# Templates
templates = Jinja2Templates(directory="templates")

# Global variable to store current blog results
current_blog = None

# Initialize MCP components
def get_mcp_agent():
    """Initialize MCP agent with OpenAI model"""
    llm = ChatOpenAI(
        model="gpt-4o-mini",
        temperature=0.7,
        api_key=os.getenv("OPAPIKEY")
    )
    # llm = ChatGroq(
    #     model="llama-3.3-70b-versatile",
    #     api_key=os.getenv("GROQ_API_KEY")
    # )
    client = MCPClient.from_config_file("multiserver_setup_config.json")
    agent = MCPAgent(
        llm=llm,
        client=client,
        use_server_manager=False,
        max_steps=30
    )
    return agent

@app.get("/", response_class=HTMLResponse)
async def home(request: Request):
    """Home page with blog topic form"""
    return templates.TemplateResponse("index.html", {"request": request})

@app.post("/search")
async def perform_web_search(
    request: Request,
    topic: str = Form(...)
):
    """Perform web search using MCP linkup server"""
    try:
        agent = get_mcp_agent()
        
        # Step 1: Perform web search
        print(f"🔍 Performing web search for: {topic}")
        search_result = await agent.run(
            f"Use the 'linkup' server to search for: {topic}"
        )
        
        if search_result:
            return templates.TemplateResponse(
                "search_results.html",
                {
                    "request": request,
                    "topic": topic,
                    "search_results": search_result,
                    "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
                }
            )
        else:
            raise HTTPException(status_code=500, detail="Search failed")
    except Exception as e:
        return templates.TemplateResponse(
            "error.html",
            {
                "request": request,
                "error": str(e),
                "topic": topic
            }
        )

@app.post("/generate_blog")
async def generate_blog_post(
    request: Request,
    topic: str = Form(...),
    search_results: str = Form(...)
):
    """Generate blog post from search results and save to filestore"""
    global current_blog
    try:
        agent = get_mcp_agent()
        
        llm = ChatGroq(
            model="deepseek-r1-distill-llama-70b",
            temperature=0.7,
            api_key=os.getenv("GROQ_API_KEY")
        )
        # Step 2: Generate blog post
        print(f"📝 Generating blog post for: {topic}")
        blog_post = await llm.ainvoke(
            f"Write an engaging blog post about the topic: {topic} based on the search results: {search_results} Use emojis and make it interesting."
        )
        
        if blog_post:
            # Step 3: Save blog post to filestore
            print(f"💾 Saving blog post to filestore...")
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            filename = f"blog_{topic.replace(' ', '_').lower()}_{timestamp}.md"
            
            save_result = await agent.run(
                f"Use the tool `write_file` from the `filesystem` server and write filename: '{filename}' at filestore directory and save  content: {blog_post.content}"
            )
            
            result = {
                "topic": topic,
                "search_results": search_results,
                "blog_post": blog_post.content,
                "filename": filename,
                "save_result": save_result
            }
            current_blog = result
            
            return templates.TemplateResponse(
                "results.html",
                {
                    "request": request,
                    "blog": result,
                    "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
                }
            )
        else:
            raise HTTPException(status_code=500, detail="Blog generation failed")
    except Exception as e:
        return templates.TemplateResponse(
            "error.html",
            {
                "request": request,
                "error": str(e),
                "topic": topic
            }
        )

@app.get("/results")
async def view_results(request: Request):
    global current_blog
    if not current_blog:
        return RedirectResponse(url="/", status_code=303)
    return templates.TemplateResponse(
        "results.html",
        {
            "request": request,
            "blog": current_blog,
            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        }
    )

@app.get("/download/{filename}")
async def download_file(filename: str):
    file_path = f"filestore/{filename}"
    if not os.path.exists(file_path):
        raise HTTPException(status_code=404, detail="File not found")
    from fastapi.responses import FileResponse
    return FileResponse(file_path, filename=filename)

@app.get("/api/status")
async def api_status():
    return {
        "status": "running",
        "timestamp": datetime.now().isoformat(),
        "has_blog": current_blog is not None
    }

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="127.0.0.1", port=8000)

🔬📚

Troubleshooting Common Issues

Windows Compatibility Issues

Problem: [WinError 193] %1 is not a valid Win32 application

Solution: Use full paths to executables in MCP configuration:

“command”: “C:\\Program Files\\nodejs\\npx.cmd”

MCP Server Connection Issues

Problem: MCP servers fail to connect

Solution:

1. Ensure servers are installed globally

2. Check API keys in environment variables

3. Verify server commands in configuration file

Template Rendering Issues

Problem: Jinja2 template errors

Solution: Install Jinja2 explicitly:

pip install jinja2

Performance and Optimization

Current Performance Metrics

• Search Response Time: 3–5 seconds

• Blog Generation Time: 10–15 seconds

• File Save Time: 1–2 seconds

• Total Workflow Time: 15–25 seconds

Optimization Strategies

1. Caching: Implement caching search results

2. Async Processing: Use background tasks for long-running operations

3. Connection Pooling: Optimize MCP server connections

4. CDN Integration: Serve static assets via CDN

Security Considerations

API Key Management

• Store API keys in environment variables

• Never commit API keys to version control

• Use secure key management services in production

Input Validation

• Validate user inputs to prevent injection attacks

• Sanitize search queries and blog topics

• Implement rate limiting for API endpoints

File System Security

• Restrict file system access to specific directories

• Validate file paths to prevent directory traversal attacks

• Implement file size limits and type restrictions

Conclusion

The MCP Blog Generator demonstrates the power and flexibility of the Model Context Protocol in building intelligent web applications. By combining multiple MCP servers with advanced AI models, we’ve created a system that can:

• Automate content research through intelligent web search

• Generate high-quality content using state-of-the-art AI models

• Manage files seamlessly through MCP filesystem integration

• Provide an intuitive user experience with modern web technologies

Key Achievements

1. Seamless Integration: Successfully integrated multiple MCP servers with AI models

2. Production Ready: Built a complete web application with proper error handling

3. User Friendly: Created an intuitive interface for non-technical users

4. Extensible Architecture: Designed for easy addition of new features and servers

Learning Outcomes

• Understanding of MCP architecture and server integration

• Experience with modern AI model APIs and content generation

• Knowledge of FastAPI web development and async programming

The project showcases how emerging technologies like MCP can be combined with established web development practices to create powerful, intelligent applications that enhance productivity and creativity.