Task Tracker MCP Server: Integrating Linear and TrackingTime

Overview: What is Task Tracker MCP Server?

Task Tracker is an MCP (Model Context Protocol) server designed to integrate linear task management and trackingtime time tracking functionalities into AI applications. By leveraging LLMs, it enables users to automate their workflows and tasks through natural language commands. The ultimate goal is to enhance the efficiency of using AI applications like Claude Desktop by providing a unified interface for accessing various tools and data sources.

🔧 Core Features & MCP Capabilities

Linear Integration

Task Tracker supports integration with the Linear API, allowing the creation, management, updating, and querying of tasks in real-time. Some key features include:

• Creation: Generate new tasks with optional details such as project assignment, description, and status.
• Maintenance: Update task statuses directly within the command-line interface or through LLM prompts to reflect real-world progress accurately.
• Querying: Retrieve tasks based on multiple filters including title for detailed project tracking.

TrackingTime Integration

For seamless time-tracking, Task Tracker offers comprehensive support:

• Start Time Tracking: Initiate timers for specific tasks, marking them active and start recording time against that task.
• Stop Time Tracking: Pause the timer to prevent overcharging or track a segment of work precisely.
• View Activity: Inspect ongoing time tracking activities to ensure accurate billing or project costing.

⚙️ MCP Architecture & Protocol Implementation

Task Tracker implements the Model Context Protocol (MCP) to enable communication between AI applications and external services. The server configuration ensures secure and efficient data exchange, facilitating robust interactions with supported tools like Linear and TrackingTime:

Flow Diagram

The following Mermaid diagram illustrates the flow of messages and data exchanged between an MCP client and Task Tracker.

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

Compatibility Matrix

The table below outlines the current support for various MCP clients, highlighting which tools and features are available.

MCP Client	Linear	TrackingTime	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

🚀 Getting Started with Installation

To set up Task Tracker MCP Server, follow these steps:

1. Environment Setup: Ensure your development environment is properly configured as per the .env.example configuration.
2. Install Dependencies: Run sh scripts/setup.sh to build and install necessary packages.
3. Run Server: Execute either uv build or uv run task-tracker directly.

{
  "mcpServers": {
    "task-tracker": {
      "command": "uv",
      "args": ["--directory", "/path/to/task-tracker", "run", "task-tracker"],
      "env": {"API_KEY": "your-api-key"}
    }
  }
}

💡 Key Use Cases in AI Workflows

Real-time Task Management

AI applications like Claude Desktop can integrate with Task Tracker to monitor and manage tasks within a Linear project. Users can instruct the LLM to create, update, or query tasks directly, streamlining task assignment and progress tracking.

graph LR;
    A[Create New Task] --> B{Is Task Required?};
    B -- Yes --> C[Start Timer];
    C --> D[Task Completed?];
    D -- No --> B;
    D -- Yes --> E[Update Task Status to Done];

Time Tracking Automation

With the integration of TrackingTime, developers can automate time tracking for tasks. By initiating and stopping timers via LLM commands, users can track time spent on each task accurately.

graph LR;
    A[Start Timer] --> B{Is Time-Tracking Needed?};
    B -- Yes --> C[Record Time Spent];
    C --> D[Stop Timer];
    D --> E[Update Task Status with Time Expended];

🔌 Integration with MCP Clients

Task Tracker MCP Server supports seamless integration with the following AI applications:

• Claude Desktop: Full support for task management and time tracking.
• Continue: Limited support due to incompatibilities, but still useful for basic functionality.
• Cursor: Limited compatibility; primary use is data source access rather than direct task creation.

📊 Performance & Compatibility Matrix

Task Tracker ensures high performance and compatibility with the following tools:

• Linear API: Full API integration.
• TrackingTime SDK: Integration via API calls.

🛠️ Advanced Configuration & Security

MCP Configuration Sample

Here is an example of how to configure Task Tracker for MCP clients:

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

Security Considerations

Task Tracker includes security measures such as API key management to protect against unauthorized access. Ensure your environment variables are well-protected and restricted from public exposure.

❓ Frequently Asked Questions (FAQ)

1. Can Task Tracker be used with other AI applications besides those specified in the matrix?

   • While the current MCP configuration primarily supports specified clients, integrating with additional MCP clients is possible through custom modifications.

2. How do I ensure data privacy and security when using Task Tracker with Linear and TrackingTime?

   • Utilize API keys and secure environment variables to protect sensitive information. Regularly update your dependencies to mitigate potential vulnerabilities.

3. Is it possible to customize the tasks created or managed by the server?

   • Yes, you can extend the Create New Task feature in Task Tracker by adding custom fields and parameters via MCP protocol calls.

4. How does Task Tracker handle time tracking for complex projects with multiple sub-tasks?

   • The server allows for detailed task breakdowns, enabling accurate time tracking even for large-scale projects with numerous sub-tasks.

5. Can I use Task Tracker in environments where internet connectivity is limited or non-existent?

   • The server supports offline operation by storing tasks and times locally until a network connection becomes available.

👨‍💻 Development & Contribution Guidelines

For developers interested in contributing to Task Tracker, we welcome your participation! Please follow these steps:

1. Fork the Repository: Clone the project onto your local machine.
2. Set Up the Environment: Follow the provided .env.example file and sh scripts/setup.sh.
3. Contribute Code: Make changes or improvements where needed and submit a pull request.

🌐 MCP Ecosystem & Resources

To learn more about MCP and explore other resources, check out:

• Model Context Protocol Documentation: Official Website
• GitHub Repository: Task Tracker GitHub
• Community Forums: Join the community discussions on forums like Stack Overflow or GitHub to connect with other developers.

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This documentation aims to provide a comprehensive guide for integrating Task Tracker MCP Server into AI workflows, ensuring seamless tool integration and enhanced efficiency.