mcp-server

Creating my new mcp server Build Your First MCP Server: Leave Management This AI tool helps an HR with leave management related tasks. The codebase here is for MCP server that interacts with mock leave database and responds to MCP client queries.

Overview: What is mcp-server?

The mcp-server is a versatile adapter specifically designed for integrating human resources (HR) data into the Model Context Protocol (MCP). It facilitates interaction between an AI application like Claude Desktop, and a leave management system. By leveraging the MCP protocol, this server enables seamless data exchange, making it easier to manage leave requests and related processes through an automated workflow.

🔧 Core Features & MCP Capabilities

The mcp-server possesses several key features that make it indispensable for AI applications aiming to integrate complex HR systems:

• Data Interaction: The server supports CRUD (Create, Read, Update, Delete) operations on a mock leave database.
• Standardized Communication: It adheres strictly to the MCP protocol, ensuring compatibility with various AI clients such as Claude Desktop and Continue.
• Configuration Flexibility: Users can customize the behavior of the server using command-line arguments and environment variables.

MCP Protocol Implementation

The mcp-server implements the Model Context Protocol (MCP) by providing a standardized interface for interacting with data sources. This protocol includes messages, structures, and procedures that align perfectly with the AI client's operational model. For instance, when an AI application sends a request to fetch leave records, the protocol ensures that the request is correctly formed and the response is appropriately structured.

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

Real-world AI Workflow Scenario

Imagine an HR manager using Claude Desktop to process leave applications. The mcp-server interacts with the mock leave database, fetching and updating records as needed. This seamless interaction enhances the efficiency of administrative tasks, streamlining processes such as approving or denying leave requests based on predefined policies.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the mcp-server revolves around the MCP protocol's strict framework for data exchange. The server's core functionality is driven by a combination of custom code and pre-built components that adhere to the standards set forth by the protocol. By integrating with Claude Desktop, it ensures compatibility across different environments.

This MCP server follows a client-server model where the AI application (like Claude Desktop) acts as the MCP client, sending requests over the network to interact with the server. The server then processes these requests and returns the appropriate data or actions as a response.

Real-world Example Implementation

async def handle_leave_request(request: LeaveRequest):
    # Fetch leave records from mock database
    await database.get_records(request.employee_id)
    
    # Process request according to HR policy
    decision = decide_on_leave_request(request, policies)

    # Return the decision back to MCP client
    return json.dumps({"decision": decision})

🚀 Getting Started with Installation

To get started with setting up your own mcp-server, follow these steps:

1. Install Claude Desktop: Ensure you have Claude Desktop installed as the primary interface for interacting with AI services.

2. Set Up uTUV: Install uv via pip by running the command: pip install uv.

3. Initialize Project Directory:

   uv init my-first-mcp-server
   

4. Add MCP CLI to Project:

   uv add "mcp[cli]"
   

5. Upgrade TypeErrors: If you encounter any type errors, upgrade the typer library by running: pip install --upgrade typer.

6. Write Code for Leave Management Server, and ensure your codebase is placed in a directory named main.py.

7. Install the Server into Claude Desktop:

   uv run mcp install main.py
   

8. Kill Running Instances: Terminate any running instances of Claude from the Task Manager.

9. Restart Claude Desktop: Open Claude Desktop to see new tools and features.

💡 Key Use Cases in AI Workflows

1. HR Management: Efficiently manage leave requests, vacation days, and other HR-related operations through seamless integration with advanced AI tools.
2. Automated Decision Making: Automate leave approvals based on predefined policies, reducing manual labor and improving decision consistency.

MCP Protocol Flow Diagram

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

Integration with MCP Clients

The mcp-server supports a range of MCP clients, including Claude Desktop and Continue. Each client is designed to adhere to the MCP protocol's specifications, ensuring reliable and secure data exchange.

| MCP Client | Resources | Tools | Prompts | Status |
|------------|-----------|-------|---------|---------|
| Claude Desktop | ✅ | ✅ | ✅ | Full Support |
| Continue | ✅ | ✅ | √ | Full Support |
| Cursor | ❌ | ✕ | ❌ | Limited functionality (Tools Only) |

🔌 Integration with MCP Clients

The mcp-server is versatile and can be integrated seamlessly with various MCP clients. Here’s how to integrate it with Claude Desktop:

1. Install Required Dependencies: Ensure you have uv installed via pip.
2. Initialize Project Directory: Use the command: uv init my-first-mcp-server.
3. Add MCP CLI: Run uv add "mcp[cli]" to install the necessary CLI tools.
4. Write Code for Leave Management Server: Define your leave management logic in main.py, ensuring it adheres to the protocol's standards.

Performance & Compatibility Matrix

The performance of the mcp-server is optimized for real-time data processing and high throughput. The server ensures reliable communication with various MCP clients, providing rapid responses even under heavy load conditions.

While the server is compatible across multiple platforms, some limitations exist:

• Cursor: Limited integration due to missing prompts.
• Other MC Clients: Full compatibility with resources and tools.

🛠️ Advanced Configuration & Security

For advanced users and developers, the mcp-server offers additional configuration options:

• Environment Variables: Customize behaviors by setting environment variables like API_KEY.
• Security Settings: Ensure data security through SSL/TLS encryption and authentication mechanisms.

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

❓ Frequently Asked Questions (FAQ)

1. Question: Can the mcp-server be integrated with multiple MCP clients simultaneously?

   • Answer: Yes, it can support integration with multiple clients provided they comply with the MCP protocol.

2. Question: How do I resolve common errors during installation?

   • Answer: Ensure you have all necessary dependencies installed and check for version compatibility issues.

3. Question: Can I customize the leave management logic in main.py?

   • Answer: Absolutely, users can extend or modify the leave management logic to meet specific requirements.

4. Question: What security measures are in place?

   • Answer: The server uses SSL/TLS for secure data transmission and strong authentication methods to protect sensitive information.

5. Question: How do I troubleshoot issues with MCP protocol compatibility?

   • Answer: Check the MCP client documentation and ensure all requests adhere strictly to protocol specifications.

👨‍💻 Development & Contribution Guidelines

Developers are encouraged to contribute to the mcp-server by:

• Fixing bugs
• Improving performance
• Adding new features

Contributions can be submitted via pull requests, following best practices in code style and documentation.

🌐 MCP Ecosystem & Resources

For more information on the Model Context Protocol and related resources, visit MCP Documentation or join the MCP Community.

By leveraging the mcp-server and its comprehensive integration capabilities, developers can create powerful AI applications that enhance HR workflows and operations.