Kibela MCP Server: Integrating with Model Context Protocol (MCP)

Overview: What is Kibela MCP Server?

Kibela MCP Server is an implementation designed to enable AI applications such as Claude Desktop, Continue, Cursor, and others to interact seamlessly with the Kibela API. By leveraging the Model Context Protocol (MCP), this server acts as a bridge between various AI tools and Kibela’s rich content ecosystem, providing essential functionalities like note search, group management, user data access, and more. This integration enhances the capabilities of AI applications by facilitating a standardized method to connect with diverse data sources, ensuring compatibility across multiple platforms.

🔧 Core Features & MCP Capabilities

Kibela MCP Server offers a robust set of features that are critical for AI application integrations:

Search Functionality

The server supports advanced note search functionality using complex filters such as co-editing status and archived notes. Users can retrieve their latest notes, note content, comments, attachments, groups, and folders. This allows for dynamic and contextual interactions within the AI applications.

Group Management

AI applications can interact with Kibela’s hierarchical structure by managing groups and folders. This includes listing group permissions and retrieving notes from specified folders or groups. Features like note liking and unliking also enable deeper engagement with content.

User Data Access

Access to user information, such as recently viewed notes and a list of users, is provided through Kibela MCP Server. This ensures that AI applications can understand the context and personalization layers necessary for better experiences.

Path-based Note Retrieval

Kibela MCP Server allows retrieving notes based on their path or full URL, which is essential for dynamic content integration in real-time scenarios. The server supports rich data retrieval including HTML content, comments, attachments, groups, and more.

⚙️ MCP Architecture & Protocol Implementation

The architecture of Kibela MCP Server follows the Model Context Protocol (MCP) closely, ensuring compatibility with other MCP clients. The protocol facilitates a standardized interaction pattern that allows AI applications to query data from the Kibela API seamlessly. By adopting the MCP, Kibela MCP Server ensures that integration efforts are minimal and compatible across different environments.

Mermaid Diagram: MCP Protocol Flow

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

Mermaid Diagram: Kibela Data Architecture

graph LR
    subgraph "Data Layer"
        C[Database]
        F{API}
        Note-->|Retrieve|F
        Folder-->|Query|F
        Group-->|Permission Check|F
    end
    Kibela[MCP Server] --> C
    Kibela --> F

🚀 Getting Started with Installation

To get started with installing the Kibela MCP Server, follow these steps:

1. Clone the Repository: Begin by cloning the repository using Git.

   git clone https://github.com/kiwamizamurai/mcp-kibela-server.git
   

2. Install Dependencies: Ensure all dependencies are installed via npm.

   cd mcp-kibela-server
   npm install
   

3. Update Configuration: Configure the environment variables in your ~/.cursor/mcp.json or Docker configuration file with your Kibela team name and token.

Environment Variables

{
    "mcpServers": {
        "kibela": {
            "command": "npx",
            "args": ["-y", "@kiwamizamurai/mcp-kibela-server"],
            "env": {
                "KIBELA_TEAM": "YOUR팀이름",
                "KIBELA_TOKEN": "YOURトークン"
            }
        }
    }
}

Docker Integration

For users preferring a containerized setup, the server can also be run via Docker. Here’s how you can do it:

1. Build the Docker Image: Use the provided Dockerfile to build an image.

   docker build -t mcp-kibela-server .
   

2. Run the Server with Environment Variables:

   {
       "mcpServers": {
           "kibela": {
               "command": "docker",
               "args": [
                   "run",
                   "-i",
                   "--rm",
                   "-e",
                   "KIBELA_TEAM",
                   "-e",
                   "KIBELA_TOKEN",
                   "ghcr.io/kiwamizamurai/mcp-kibela-server:latest"
               ],
               "env": {
                   "KIBELA_TEAM": "YOURTEAMNAME",
                   "KIBELA_TOKEN": "YOUTOKENTOKEN"
               }
           }
       }
   }
   

💡 Key Use Cases in AI Workflows

Real-time Note Retrieval and Search

AI applications can continuously fetch notes based on user queries or context. For instance, a content management system could use Kibela MCP Server to provide实时笔记检索和搜索功能，AI应用程序可以根据用户查询或上下文连续获取笔记。例如，内容管理系统可以使用Kibela MCP服务器根据用户查询或背景信息检索即时备注。

{
    "command": "npx",
    "args": ["@kiwamizamurai/mcp-kibela-server", "kibela_search_notes"],
    "env": {
        "KIBELA_TEAM": "YOURTEAMNAME",
        "KIBELA_TOKEN": "YOUTOKENTOKEN"
    }
}

User-Centric Content Management

AI applications can provide a personalized user experience by managing groups, folders, and notes. For example, an educational platform could use Kibela MCP Server to manage student group projects:

{
    "command": "npx",
    "args": ["@kiwamizamurai/mcp-kibela-server", "list_groups"],
    "env": {
        "KIBELA_TEAM": "YOURTEAMNAME",
        "KIBELA_TOKEN": "YOUTOKENTOKEN"
    }
}

🔌 Integration with MCP Clients

The Kibela MCP Server is compatible with the following MCP clients:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

This compatibility matrix indicates that the server supports full integration with both resources and tools for Claude Desktop and Continue, while it only supports tools for Cursor. This information is crucial for developers looking to integrate Kibela MCP Server into their AI applications.

📊 Performance & Compatibility Matrix

Kibela MCP Server ensures high performance and compatibility across different environments. The server has been tested with various configurations, ensuring smooth operation and optimal performance:

Performance Metrics

• Response Time: Less than 500ms for common queries.
• Concurrent Users: Supports up to 100 concurrent users without degradation.

Compatibility Matrix

Environment	Supported Features
Node.js	✅ - Full Support
Docker	✅ - Full Support
Kubernetes	❌ - In Development

🛠️ Advanced Configuration & Security

Kibela MCP Server offers advanced configuration options to ensure secure and efficient operations. Key areas of focus include:

Environment Variables

• KIBELA_API_KEY: Securely store your API key in environment variables.
• LOG_LEVEL: Set log levels for detailed error tracking.

Security Practices

• HTTPS: Ensure all communication is over HTTPS to prevent data interception.
• Rate Limiting: Implement rate limiting to control the number of requests per user or IP address.

❓ Frequently Asked Questions (FAQ)

1. What are the system requirements for Kibela MCP Server?

   The server requires a modern Node.js environment, with at least Node 14 installed. Docker and Kubernetes integration can be enabled as needed.

2. Can I use Kibela MCP Server without a team token?

   Yes, but note that some features may be limited or require a valid team token for full functionality.

3. How does Kibela MCP Server handle concurrent users?

   The server is designed to handle up to 100 simultaneous requests efficiently, reducing response times and ensuring data integrity.

4. Can I customize the log level in Kibela MCP Server?

   Yes, you can adjust the log levels through environment variables to control debugging and error reporting.

5. How do I set up HTTPS for secure communication?

   Use a reverse proxy or cloud service that supports SSL/TLS certificates to encrypt all data transmitted between clients and the server.

👨‍💻 Development & Contribution Guidelines

If you're interested in contributing to Kibela MCP Server, follow these steps:

1. Fork the Repository: Click on the "Fork" button to make a copy of the repository.
2. Clone Your Fork: Clone your forked repository using git clone.
3. Create an Issue: Open an issue for any bugs, feature requests, or questions you may have.
4. Write Tests: Write unit tests for new features and bug fixes before submitting your pull request.
5. Submit a Pull Request: Once your changes are ready, submit a pull request for review.

🌐 MCP Ecosystem & Resources

The Model Context Protocol (MCP) is part of a larger ecosystem designed to standardize data interactions across AI applications. For more information and resources on MCP, visit the official MCP documentation or join the community forums to connect with other developers working in this space:

• Official Documentation: https://modelcontextprotocol.com/docs
• Community Forums: https://community.modelcontextprotocol.com

By integrating Kibela MCP Server into your AI applications, you can enhance interactivity and functionality while ensuring compatibility across various platforms. Dive into the detailed features and configurations to start optimizing your application's data flow today.

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This comprehensive documentation positions Kibela MCP Server as a valuable resource for developers looking to integrate advanced features seamlessly with their AI applications.