Tama - AI-Powered Task Manager CLI MCP Server

@Gitreceiver

代码仓库

基本信息

分类

开发工具

许可证

MIT

传输方式

stdio

作者

Gitreceiver

关于 Tama - AI-Powered Task Manager CLI MCP Server

AI-Powered Task Manager CLI with MCP Server

Tama - AI-Powered Task Manager CLI MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "TAMA-MCP": {
      "command": "uv",
      "args": [
        "venv",
        "-p",
        "3.12"
      ]
    }
  }
}

Tama - AI-Powered Task Manager CLI MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Tama?

Tama is a command-line interface (CLI) tool for task management, enhanced with AI capabilities for task generation and decomposition. It uses an OpenAI-compatible API configured for DeepSeek models to parse product requirements documents (PRDs) and break down complex tasks into manageable subtasks.

How to use Tama?

Install Tama with pip in a Python 3.12 virtual environment, then configure a .env file with your DeepSeek API key and model settings. Run commands like tama add, tama list, tama expand, and tama prd in the terminal. Tama can also be used as an MCP server by running uv --directory /path/to/TAMA_MCP run python -m src.mcp_server.

Key features of Tama

Standard task management with dependency tracking
AI-powered PRD parsing and task decomposition
SQLite database for persistent data storage
Git integration for automatic branch and commit creation
Code stub generation from task details
Dependency cycle detection and visualization
Markdown and Mermaid report generation

Use cases of Tama

Parse a product requirements document to automatically generate a structured task list
Break down high-level tasks into detailed subtasks using AI
Track dependencies between tasks and detect circular dependencies
Generate placeholder code files based on task descriptions
Identify the next actionable task based on status and dependencies

常见问题

What dependencies or runtime does Tama require?

Tama requires Python 3.12 and uses a virtual environment. It depends on the `tama-cli` package, SQLite for storage, and an OpenAI-compatible API key (DeepSeek models are configured by default).

How does Tama handle data persistence?

All task data is stored in a local SQLite database, ensuring stable and reliable data operations.

Can Tama be used programmatically by other applications?

Yes, Tama can be run as an MCP server over stdio transport, exposing tools like `list_tasks`, `add_task`, and `set_status` for external integration.

Does Tama support any shell auto‑completion?

Yes, you can set up shell auto‑completion by running `tama --install-completion`.

What AI models does Tama use?

Tama is configured to use DeepSeek models through an OpenAI-compatible API. The configuration requires a `DEEPSEEK_API_KEY` and optionally sets general and reasoning model names.

开发工具分类下的更多 MCP 服务器

OrangePro

Aamir Siddiqui

OrangePro local-first CLI + MCP server for behavior mapping, grounded test generation, and dynamic proof.

Screen Browser

Turn a plain-language guide into a narrated walkthrough video of your deployed web app. Your coding agent can write the guide from the source and bring back the MP4.