Time MCP Server: A Versatile Tool for Large Language Models

Overview: What is Time MCP Server?

The Time MCP Server is an essential component in the Model Context Protocol (MCP) ecosystem, designed to provide time-related utilities and tools for Large Language Models (LLMs). Built on FastMCP 2.0 framework, it offers a range of functionalities that enhance AI applications by integrating them with real-time data sources. This server supports various MCP clients, including Claude Desktop, Continue, Cursor, and others through its comprehensive API and protocol.

🔧 Core Features & MCP Capabilities

The Time MCP Server’s core architecture is built around the Model Context Protocol (MCP), a universal adapter that streamlines integration between AI applications and external data sources. Key features and MCP capabilities include:

1. Tool Integration: The server integrates with LLMs through a set of predefined tools, such as get_current_time and get_time_components.
2. Transport Protocols: Supports both stdio (standard input/output) and streamable HTTP transport protocols for versatile use cases.
3. Customizability: Allows developers to configure the server via command-line arguments and environment variables.
4. Extensibility: Enables easy addition of new tools, enhancing the versatility and flexibility of AI applications.
5. Security & Reliability: Implements best practices in protocol security and reliability through robust validation mechanisms.

⚙️ MCP Architecture & Protocol Implementation

The Time MCP Server is architected to follow the Model Context Protocol (MCP), ensuring seamless integration with various AI applications. It is built using FastMCP 2.0, which provides a standardized API for interacting with LLMs. The server's architecture is modular and extensible, allowing for easy updates and maintenance.

The protocol flow diagram illustrates how data flows between the AI application, MCP client, Time MCP Server, and external tools or data sources:

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 highlights the key components and their interactions, ensuring clear communication and data processing.

🚀 Getting Started with Installation

To get up and running with the Time MCP Server, follow these steps:

1. Clone the Repository: Begin by cloning the repository using git clone.

2. Set Up the Environment:

   • Install uv for package management: pip install uv.
   • Use task for managing tasks: npm install taskfile.js --global.

3. Install Dependencies and Create Virtual Environment:

   • Run the setup command to create a virtual environment: task setup.
   • Install the package in development mode using: task install.

4. Run the MCP Server: Start the server by running:

   task run
   

Or, you can manually start it with command-line arguments for configuration:

```bash
python -m time_mcp_server.main --transport streamable-http --port 9000
```

When using task, add the flags and values after the -- option to customize the transport type and port.

💡 Key Use Cases in AI Workflows

The Time MCP Server plays a crucial role in various AI workflows, particularly those involving time context. Two of its key use cases are:

1. Real-time Date and Time Updates: LLMs can request current date and time information, enabling them to provide more accurate and timely responses. This is especially useful for applications requiring up-to-date temporal data.
2. Time Component Extraction: Tools like get_time_components allow LLMs to break down datetime into its constituent parts, facilitating complex date-time manipulations.

🔌 Integration with MCP Clients

The Time MCP Server is highly compatible with several MCP clients, including:

• Claude Desktop - Full support for stdio, streamable-http, and http-stream transports.
• Continue - Supports both stdio and streamable-http through direct command-line interaction.
• Cursor - Limited integration; currently supports only stdio.

The following is a configuration matrix showcasing the compatibility:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅ (standard: yes)	✅ (available)	✅ (no issues)	Full Support
Continue	✅	✅ (limited API access)	❌ (not supported directly)	Partial Integration
Cursor	❌	✅(command-line only)	❌ (unsupported)	Tools Only

Through this integration, developers can leverage the Time MCP Server to enhance their LLMs' capabilities across various applications.

📊 Performance & Compatibility Matrix

The performance and compatibility matrix provides an overview of how well the Time MCP Server works with different clients:

Client Name	Transport Protocols Supported
Claude Desktop	stdio, streamable-http
Continue	stdio, streamable-http
Cursor	stdio only

This matrix highlights the supported transport protocols, ensuring seamless interaction between the server and its clients.

🛠️ Advanced Configuration & Security

To ensure smooth operation and security of the Time MCP Server, follow these advanced configuration practices:

• Code Formatting: Use task format to keep your code in line with community standards.
• Linting:
  • Check for linting issues using task check.
  • Run unit tests with task test to ensure everything is functioning correctly.

These practices help maintain the reliability and integrity of the server.

❓ Frequently Asked Questions (FAQ)

Q1: How do I install the Time MCP Server?

To install, clone the repository and run:

task setup && task install

This command sets up the environment and installs dependencies.

Q2: Can I use the Time MCP Server with other clients besides Claude Desktop?

Yes, the server supports multiple clients including Continue and Cursor. Refer to the compatibility matrix for details on supported transports and features.

Q3: What are the available tools in the Time MCP Server?

The server provides two main tools:

1. get_current_time: Get the current date and time with optional formatting.
2. get_time_components: Break down the current datetime into its components.

Q4: How do I run the Time MCP Server with a specific transport protocol?

You can start the server with:

task run -- --transport streamable-http --port 9000

This configuration uses the HTTP protocol on port 9000.

Q5: Is there a sample configuration file for setting up an MCP client?

Yes, here is a sample configuration snippet to include in your client's settings:

{
  "mcpServers": {
    "time-mcp-server": {
      "command": "/path/to/uv",
      "args": [
        "run", "--directory", "/path/to/time-mcp-server", "python", "-m", "time_mcp_server.main"
      ]
    }
  }
}

Replace the paths with your actual configuration.

👨‍💻 Development & Contribution Guidelines

Contributions to the Time MCP Server are welcome. To contribute:

1. Environment Setup:

   • Install uv and task.
   • Clone the repository.

2. Contribute Code:

   • Fork the repository on GitHub.
   • Make your updates in a new branch.
   • Run checks and tests using provided tasks (format, check, test).
   • Submit a pull request.

Your contributions are vital to improving this important tool for AI applications.

🌐 MCP Ecosystem & Resources

Explore the broader Model Context Protocol (MCP) ecosystem to discover more tools, clients, and resources. The MCP protocol enables seamless integration between AI applications and external data sources, ensuring robust and secure communication.

For further documentation on the Model Context Protocol and additional resources, visit:

• ModelContextProtocol.org
• UV Package Manager Documentation
• Taskfile.js for Task Management

These resources provide a comprehensive understanding of MCP and related tools.