TAMA MCP
TAMA is an AI-enhanced command-line task management tool that supports PRD parsing, task decomposition, dependency management, etc., and provides an MCP service interface.
2 points
6.0K

What is TAMA MCP Server?

TAMA MCP Server is an extended service based on the TAMA task management CLI, providing programmatic access capabilities through a standardized Model Context Protocol interface. It encapsulates task management functions into remotely callable services and supports integration with other AI toolchains.

How to use TAMA MCP Server?

After starting the MCP server, client applications can call task management functions through the standard JSON-RPC protocol. It supports complete lifecycle operations such as task creation, deletion, modification, and query, as well as status tracking and dependency management.

Applicable Scenarios

Suitable for scenarios where task management functions need to be integrated into existing workflows, such as: - AI assistant integration - Automated script invocation - Team collaboration tool docking - CI/CD process integration

Main Features

Complete Task Management
Supports the creation, query, update, and deletion of multi-level tasks/sub-tasks, including status tracking and priority setting
Intelligent Dependency Tracking
Visualizes task dependency relationships, automatically detects circular dependencies, and supports dependency condition queries
AI-Enhanced Features
Integrates the DeepSeek model to implement PRD document parsing and intelligent task decomposition
Diverse Reports
Supports the generation of two report formats: Markdown tables and Mermaid flowcharts
Advantages
Unified JSON-RPC interface specification for easy integration
Complete task dependency management solution
Native support for AI-enhanced task decomposition and generation
Lightweight design with low resource consumption
Limitations
Requires Python environment support
AI functions depend on external API keys
Currently only supports single-machine deployment mode

How to Use

Environment Preparation
Ensure that Python 3.12 and the uv tool are installed, and clone the TAMA-MCP repository
Start the Server
Activate the virtual environment and start the MCP service in the project directory
Client Configuration
Configure the MCP server connection information in the client application

Usage Examples

AI Assistant Integration
Integrate TAMA MCP Server into the AI assistant workflow to create and manage development tasks using natural language
Automated Deployment
Check the task completion status through the MCP interface in the CI/CD process

Frequently Asked Questions

How to solve the 'ModuleNotFoundError' error?
What if the AI functions cannot be used?
How to extend custom functions?

Related Resources

GitHub Repository
Project source code and the latest version
DeepSeek API Documentation
API reference for AI functions
MCP Protocol Specification
Standard definition of the Model Context Protocol

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

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