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.
rating : 2 points
downloads : 8
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 integrationMain Features
Complete Task ManagementSupports the creation, query, update, and deletion of multi-level tasks/sub-tasks, including status tracking and priority setting
Intelligent Dependency TrackingVisualizes task dependency relationships, automatically detects circular dependencies, and supports dependency condition queries
AI-Enhanced FeaturesIntegrates the DeepSeek model to implement PRD document parsing and intelligent task decomposition
Diverse ReportsSupports the generation of two report formats: Markdown tables and Mermaid flowcharts
Advantages and Limitations
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 IntegrationIntegrate TAMA MCP Server into the AI assistant workflow to create and manage development tasks using natural language
Automated DeploymentCheck 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
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