Task Tracker MCP Server
📋 任务追踪器 MCP 服务器
一个使用 FastMCP 构建并通过 uv 管理的实用 模型上下文协议 (MCP) 服务器,基于 Python 实现。该服务器为 AI 助手(如 Claude Desktop、Cursor、Antigravity 等)提供了结构化的任务管理功能。
🌟 概述
模型上下文协议 (MCP) 是一种开放标准,允许 LLM 和 AI 应用程序安全、无缝地与外部工具和数据源交互。
本项目实现了 MCP 的三个核心原语:
🛠️ 工具 (Tools):可调用的函数,允许模型执行操作(
add_task、complete_task、delete_task)。📦 资源 (Resources):只读数据 URI,允许模型检查状态(
tasks://all、tasks://pending)。💡 提示 (Prompts):预定义的提示模板,指导 AI 执行复杂的工作流程(例如,任务分析与优先级排序)。
flowchart LR
Host["AI Host / Application<br/>(Claude Desktop / Cursor / Antigravity)"]
Client["MCP Client<br/>(Protocol Handler)"]
Server["Task Tracker MCP Server<br/>(FastMCP)"]
Host <--> Client
Client <--> Server
subgraph ServerCapabilities ["Server Capabilities"]
Tools["🛠️ Tools<br/>add_task, complete_task, delete_task"]
Resources["📦 Resources<br/>tasks://all, tasks://pending"]
Prompts["💡 Prompts<br/>task_summary_prompt"]
end
Server --- ServerCapabilities🚀 功能与 MCP 原语
1. 工具(操作)
工具 | 参数 | 描述 |
|
| 添加一个新任务,带有唯一 ID 和 ISO 时间戳。 |
|
| 将任务状态标记为 |
|
| 按 ID 删除任务并返回被删除的对象。 |
2. 资源(只读数据)
资源 URI | 描述 |
| 格式化并返回所有任务,带表情符号( |
| 过滤并仅返回活动/待处理的任务。 |
3. 提示(引导式工作流程)
提示 | 描述 |
| 引导 AI 助手分析待处理与已完成的任务,识别逾期项目,并使用 |
📦 使用 uv 开始
本项目使用 uv 构建和管理,这是一款由 Astral 用 Rust 编写的极速 Python 包和项目管理器。
1. 安装 uv
macOS / Linux:
curl -LsSf https://astral.sh/uv/install.sh | shWindows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"验证安装:
uv --version2. 克隆并设置仓库
git clone https://github.com/<your-username>/task-tracker-mcp.git
cd task-tracker-mcp3. 安装依赖
uv 将自动创建虚拟环境 (.venv) 并安装所有必需的依赖项:
uv sync🧪 测试服务器
您可以运行内置的异步客户端 (test_client.py),它将执行每个工具、资源和提示:
uv run test_client.py预期输出:
🚀 Starting FastMCP Test Client...
==================================================
1. Listing Available Tools:
Found 3 tools: ['add_task', 'complete_task', 'delete_task']
2. Calling 'add_task' Tool:
Task 1 Response: {"id":1,"title":"Learn MCP", ...}
Task 2 Response: {"id":2,"title":"Master uv", ...}
3. Listing Available Resources:
Found 2 resources: [AnyUrl('tasks://all'), AnyUrl('tasks://pending')]
4. Reading 'tasks://all' Resource:
Current Tasks:
⏳ [1] Learn MCP
⏳ [2] Master uv
5. Completing Task ID 1:
Completed Result: {"id":1, "status":"completed", ...}
6. Reading 'tasks://pending' Resource:
Pending Tasks:
⏳ [2] Master uv
7. Listing Available Prompts:
Found 1 prompts: ['task_summary_prompt']
8. Deleting Task ID 2:
Delete Result: {"success": true, "deleted": {"id": 2, ...}}
==================================================
✨ All MCP Server tests completed successfully!🔌 连接与检查
1. FastMCP CLI 检查器与开发工具
FastMCP 3.x 提供了内置的 CLI 命令,用于检查和调试您的服务器:
交互式 Web 检查器:
uv run fastmcp dev inspector task_server.py检查服务器摘要:
uv run fastmcp inspect task_server.py列出所有工具:
uv run fastmcp list task_server.py运行独立服务器:
uv run fastmcp run task_server.py
2. Claude Desktop 集成
将服务器配置添加到您的 claude_desktop_config.json:
macOS:~/Library/Application Support/Claude/claude_desktop_config.json
Windows:%APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"task-tracker": {
"command": "uv",
"args": [
"--directory",
"/ABSOLUTE/PATH/TO/task-tracker-mcp",
"run",
"task_server.py"
]
}
}
}📁 项目结构
Task Tracker MCP/
├── pyproject.toml # Dependency & project metadata (managed by uv)
├── task_server.py # MCP Server definitions (tools, resources, prompts)
├── test_client.py # FastMCP async automated test client
├── src/
│ └── task_tracker_mcp/ # Python package entrypoint
│ └── __init__.py
├── .python-version # Locked Python version
├── .gitignore # Python & uv exclusions
└── README.md # Documentation💡 关键 uv 命令速查表
命令 | 描述 |
| 使用 |
| 添加依赖到 |
| 移除依赖 |
| 在隔离的项目环境中运行任何 Python 脚本 |
| 根据 |
| 显式创建虚拟环境 |
🛠️ 后续步骤与扩展
持久化存储:通过
aiosqlite或sqlite3将内存列表替换为 SQLite。优先级与截止日期:添加任务优先级标志(
low、medium、high)和截止日期过滤器。搜索工具:添加
search_tasks(query: str)工具以搜索标题和描述。身份验证:使用 FastMCP 认证提供程序保护端点。
📄 许可证
本项目基于 MIT 许可证授权 - 详见 LICENSE 文件。
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