Blog Server

A Model Context Protocol (MCP) server that provides blog management tools through a simple API interface. This server allows AI assistants to interact with a blog system to retrieve, search, and create blog posts.

🚀 Features

• Get Blogs: Retrieve all available blog posts
• Search Blogs: Search for blogs by name/query
• Create Blog: Add new blog posts to the system
• MCP Integration: Fully compatible with MCP-compatible AI assistants

🛠️ Prerequisites

• Python 3.10 or higher
• uv package manager (recommended) or pip

📦 Installation

1. Clone the repository:

   git clone <your-repo-url>
   cd mcp-by-gokogua
   

2. Create a virtual environment:

   python3.11 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install dependencies:

   uv add "mcp[cli]" httpx
   

🔧 Configuration

Claude Desktop Configuration

To use this MCP server with Claude Desktop, add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "gokogua-blog": {
      "command": "uv",
      "args": ["--directory", "/path/to/your/project", "run", "main.py"]
    }
  }
}

Note: Replace /path/to/your/project with the actual path to your project directory.

🚀 Usage

Running the Server

# Activate virtual environment
source .venv/bin/activate

# Run the MCP server
uv run main.py

Available Tools

The server provides three main tools:

1. get_blogs() - Retrieves all blog posts from the API
2. search_blogs(query: str) - Searches for blogs matching the given query
3. create_blog(name: str) - Creates a new blog post with the specified name

API Endpoint

The server connects to a mock API at:

https://6898a797ddf05523e55f7ac1.mockapi.io/blogs/Blogs

🏗️ Project Structure

mcp-by-gokogua/
├── main.py              # Main MCP server implementation
├── pyproject.toml       # Project configuration and dependencies
├── README.md            # This documentation
└── .venv/               # Virtual environment (created during setup)

🔌 MCP Integration

This server implements the Model Context Protocol (MCP) using FastMCP, providing a standardized way for AI assistants to interact with external tools and data sources.

Transport

The server uses stdio transport, making it compatible with most MCP clients.

🧪 Testing

To test the server functionality:

1. Start the server using uv run main.py
2. Use an MCP-compatible client to connect
3. Test the available tools through the MCP interface

📝 Dependencies

• mcp: Model Context Protocol implementation
• httpx: Modern HTTP client for Python
• FastMCP: Fast MCP server framework

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

📄 License

This project is open source and available under the MIT License.

🆘 Support

If you encounter any issues:

1. Check that Python 3.10+ is installed
2. Verify all dependencies are installed correctly
3. Ensure the virtual environment is activated
4. Check the API endpoint is accessible

🔗 Related Links

• Model Context Protocol (MCP)
• FastMCP Documentation
• HTTPX Documentation