Zh_MCP_Server: Model Context Protocol Server for Article Generation on Zhihu

Overview: What is Zh_MCP_Server?

Zh_MCP_Server is a specialized MCP (Model Context Protocol) server designed to facilitate interaction between AI applications and the Zhihu platform, enabling seamless article generation and publishing. This server provides an easy-to-use interface for AI model integrations like Claude Desktop, Continue, Cursor, among others, by leveraging the Model Context Protocol.

🔧 Core Features & MCP Capabilities

Zh_MCP_Server offers a powerful core set of features that ensures compatibility and functionality with multiple AI applications. The primary capability lies in its ability to connect various AI models through the Model Context Protocol, providing them access to the Zhihu platform's rich API ecosystem for content creation.

The server supports advanced configurations tailored to specific use cases, ensuring seamless integration and enhanced performance. Developers can easily deploy this server within their existing infrastructure by following a simple installation process outlined in the documentation.

⚙️ MCP Architecture & Protocol Implementation

The architecture of Zh_MCP_Server is built around the Model Context Protocol, which standardizes communication between AI applications and data sources/tools. This protocol ensures consistency across various backend environments, enabling robust performance and reliability in content generation workflows.

Mermaid Diagram: MCP Protocol Flow

graph TB
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Zhihu API/Service]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

Mermaid Diagram: Data Architecture

graph TD
    subgraph InputData
        A[User Prompt] --> B[MCP Server]
    end
    C[Zhihu API/Service] -->|Content| D[ZHIHU Posting Interface]
    E[MCP Server] --> F[AI Response]
    style InputData fill:#e1f5fe
    style ZHIHU Posting Interface fill:#e8f5e8

🚀 Getting Started with Installation

1. Environment Configuration Prerequisites

Option 1:

• Python version >= 3.10
• Dependencies as specified in requirements.txt:
  • selenium>=4.0.0
  • requests>=2.25.1
  • mcp>=0.1.0
  • webdriver-manager>=3.8.0

Install dependencies via pip:

pip install -r requirements.txt

Then, install ChromeDriver based on the specific version for your browser (e.g., @puppeteer/browsers install [email protected]).

Option 2:

• Run setup_environment.py which may require manual verification of ChromeDriver compatibility.

Clone Code Repository

git https://github.com/Victorzwx/zh_mcp_server.git

Initial Setup and Cookie Saving

Run the initialization script to save your personal cookie:

python -m zh_mcp_server.__login__

Note: This will automatically open a Chrome browser. Follow verification instructions within the browser and input received code in Terminal.

💡 Key Use Cases in AI Workflows

1. Content Generation for Marketing & SEO:
   • Developers can utilize Zh_MCP_Server to generate articles optimized for specific keywords, enhancing SEO relevance for Zhihu communities.
2. AI-Powered Content Moderation:
   • Integrate the server into a moderation system that automatically flags or approves content based on predefined templates and prompts from various AI clients.

🔌 Integration with MCP Clients

• Claude Desktop & Continue: Fully supported as they natively support MCP.
• Cursor: Only tool integration available, though future updates aim for full support.

Refer to the provided configuration example:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

📊 Performance & Compatibility Matrix

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

🛠️ Advanced Configuration & Security

If you need to debug the server operation or manipulate browser actions, disable headless mode:

poster = ZhuHuPoster(path, headless=False)  # For debugging purposes only

This can be found in server.py.

❓ Frequently Asked Questions (FAQ)

1. Why should I use Zh_MCP_Server over other solutions?
   • It provides standardized MCP integration which ensures seamless interaction between AI models and Zhihu, reducing development complexity.
2. How do I configure my environment for the best performance?
   • Ensure Python >= 3.10, follow dependencies in requirements.txt, and install ChromeDriver correctly.
3. Can multiple clients share a single instance of Zh_MCP_Server?
   • Yes, by configuring different client instances under your MCP Clients section.
4. Do I need to modify code for API key updates or changes?
   • No, simply update the environment variable in your configuration file.
5. Are there plans to add more AI clients in future releases?
   • Yes, ongoing development focuses on expanding compatibility and enhancing server functionalities.

👨‍💻 Development & Contribution Guidelines

Contributions are always welcome! Follow our Contribution Guide for detailed steps on how you can help improve the project:

• Fork the repository
• Make your changes
• Ensure tests pass
• Submit a pull request detailing your contributions

🌐 MCP Ecosystem & Resources

Connect with us and other developers across various platforms to stay updated and contribute to the growing MCP community:

• CSDN Account: Developer CSDN Blog
• WeChat Profile:

By utilizing Zh_MCP_Server, developers can streamline their AI application integration process for the Zhihu platform. This comprehensive guide aims to demystify MCP protocol usage and provide actionable insights into its implementation in AI workflows.