Curri-MCP-Server MCP Server: Enhanced Data Access and Integration for AI Applications

Overview: What is Curri-MCP-Server?

Curri-MCP-Server is a TypeScript-based MCP (Model Context Protocol) server designed to facilitate the interaction between AI applications and Curri's data resources. It serves as an intermediary, leveraging the Model Context Protocol to enable seamless integration of text note management tools into broader AI workflows. By implementing core MCP concepts, this server provides developers with a robust framework for building customized data pipelines and enhancing AI application functionalities.

🔧 Core Features & MCP Capabilities

The Curri-MCP-Server implements several key features that align with the Model Context Protocol (MCP):

1. Resources: Curri notes are represented as resources accessible via note:// URIs, each endowed with metadata including a title and content.
2. Tools: Developers can utilize provided tools like create_note, which creates new text notes based on user-specified parameters such as title and content.
3. Prompts: Prompts for generating summaries of stored notes are available, ensuring that summarized representations of data are accessible within the AI application flow.

These capabilities significantly enhance how MCP clients interact with Curri's note management system, creating a dynamic and versatile environment for AI applications to leverage structured data and automation tools.

⚙️ MCP Architecture & Protocol Implementation

The architecture of Curri-MCP-Server is meticulously crafted around MCP principles. It leverages modern TypeScript frameworks to support efficient and scalable resource management, including the storage and retrieval of note-related metadata and content.

Mermaid Diagram: Model Context Protocol (MCP) Flow

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

This diagram illustrates the flow of data and commands between an AI application, leveraging MCP Clients to interact with a Curri-MCP-Server via the MCP Protocol. The server then connects to underlying data sources or tools for processing.

Mermaid Diagram: Data Architecture

graph LR
    subgraph MCP Server ["MCP Server"]
        A[AI Application]
        B[MCP Client] -->|Requests| C[Curri-MCP-Server]
        D[[Data Storage]] --> C
    end
    subgraph Data Source/Tool ["Data Source/Tool"]
        E[[Storage API]]
        F[[Processing Engine]] --> G[MCP Server]
        C -->|Responses| F
    end

This architecture diagram shows the interaction between the MCP Client, MCP Server, and data sources/tools. It highlights how requests are handled and responses are generated.

🚀 Getting Started with Installation

To set up Curri-MCP-Server for use, follow these steps:

1. Install Dependencies: Run npm install to ensure all necessary packages are installed.
2. Build the Server: Use npm run build to compile the server's TypeScript code into JavaScript.
3. Development Setup: For a development environment with auto-rebuild capabilities, execute npm run watch.

For detailed setup and configuration instructions, refer to the README.md documentation.

💡 Key Use Cases in AI Workflows

Curri-MCP-Server enhances AI workflows by enabling the creation and management of structured data within AI environments. Here are two practical use cases:

1. Personal Knowledge Management: By integrating Curri-MCP-Server, users can create organized notes accessible via note:// URIs, making it easier to manage personal knowledge base in an AI application.
2. Document Summarization: Utilizing the summarize_notes prompt, AI applications can generate summaries of text content stored within Curri-MCP-Server, aiding in quick information retrieval and analysis.

🔌 Integration with MCP Clients

Curri-MCP-Server aims to work seamlessly with a variety of MCP clients. While support for all clients is provided, specific configurations might vary:

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

To integrate with Curri-MCP-Server, you can follow these steps for configuring MCP Clients:

{
  "mcpServers": {
    "curri-mcp-server": {
      "command": "/path/to/curri-mcp-server/build/index.js",
      "priority": 2,
      "timeout": 60000
    }
  }
}

Ensure that the configuration aligns with your AI application's requirements, particularly focusing on compatibility and performance.

📊 Performance & Compatibility Matrix

The following table provides a detailed overview of Curri-MCP-Server’s performance and compatibility matrix for various MCP clients:

Client	Command Availability	Tool Support	Prompt Support
Claude Desktop	Supported with 2-second delay	Yes	Yes
Continue	Full support	Yes	Yes
Cursor	Tools only	Yes	Not supported

🛠️ Advanced Configuration & Security

For advanced users and security-conscious deployments, the following configuration options are available:

• Server Setup: Customize environment variables, such as API_KEY, to secure access.
• Custom Commands: Modify the server's command execution with additional parameters for fine-tuning.

Ensure that your configurations adhere to best practices for data protection and interoperability within the broader MCP ecosystem.

❓ Frequently Asked Questions (FAQ)

1. How do I troubleshoot connectivity issues?
   • Check if the provided paths in claude_desktop_config.json are correct and the server is running without errors.
2. What’s the best way to secure API access through Curri-MCP-Server?
   • Use environment variables like API_KEY for securing API keys, ensuring they are managed securely.
3. Can multiple MCP clients be configured in one server?
   • Yes, by adding multiple entries under mcpServers with distinct names and paths.
4. How often should the server perform state syncs to ensure data consistency?
   • Regularly set up state sync intervals based on your application’s needs.
5. Are there specific performance optimizations for handling large note content?
   • Opt for efficient storage solutions and adjust timeouts as necessary.

👨‍💻 Development & Contribution Guidelines

Contributions are warmly welcome to enhance Curri-MCP-Server's capabilities:

1. Branching Strategy: Utilize branches for features or bug fixes.
2. Commit Messages: Follow conventional commit messages highlighting changes made.
3. Testing: Ensure all tests pass before making a pull request.

Join the community by raising issues, proposing improvements, and contributing code to the repository. Your contributions significantly shape the future of Curri-MCP-Server!

🌐 MCP Ecosystem & Resources

To learn more about Model Context Protocol (MCP), visit its official documentation at ModelContextProtocol.org. Engage with other developers and explore additional resources by joining relevant communities.

By integrating Curri-MCP-Server into AI workflows, you harness the power of structured data management, empowering your applications to offer more robust and intelligent services.