TaskMcpClient 文件任务型MCP客户端工具

1 设计目的

在各类AI聊天桌面工具（如Claude for Desktop、Cursor、Trae、CherryStuio ）中，可以方便地配置LLM模型服务和各类MCP服务端，实现LLM+MCP结合的智能体构建与测试。但测试通过后，就可能需要部署在服务器上，在LLM和MCP服务端配置妥当情况下，让程序自动执行自然语言描述的任务。由程序控制任务指令，并取走任务执行结果。

这里构想这样的任务，请求不会太频繁，大约是分钟级。采用某个目录中的Markdown文件作为输入，采用另外目录作为输出。

这就是“文件任务型MCP客户端工具”，简称“TaskMcpClient”。

另外，TaskMcpClient作为一个通用工具，从当前工作目录读取LLM和MCP配置，不依赖于其它环境和工具。

2 安装指南

在Windows或Linux系统中，执行如下命令

uv tool install TaskMcpClient

即可得到 TaskMcpClient 可执行文件，对应目录分别是

在Windows下，%HOMEPATH%\.local\bin\TaskMcpClient.exe
在Linux下，$HOME/.local/bin/TaskMcpClient

3 配置指南

创建新的工作目录，假定是<TaskDir>
在终端窗口进入到 <TaskDir>
执行 TaskMcpClient 命令，可以看到该程序的运行日志，并默认进入聊天状态。此时由于没有配置LLM，会出现APIConnectionError错误，输入quit退出命令

3.1 配置LLM

编辑 <TaskDir>/.env文件，以Ollama模型为例，配置如下：

OPENAI_BASE_URL=http://127.0.0.1:11434/v1
OPENAI_API_KEY=ollama
OPENAI_MODEL=deepseek-r1

3.2 配置MCP

3.2.1 配置运行MCP服务端的命令路径

编辑 <TaskDir>/mcp.server/cmdPath.env文件，以Window环境为例，配置如下：

uv=C:\Python3.11\Scripts\uv.exe    # uv run 按指定目录下.venv创建临时环境运行
uvx=C:\Python3.11\Scripts\uvx.exe  # uvx 等同于 uv tool run，运行Python包内命令
bun=C:\Users\Administrator\.bun\bin\bun.exe  # js运行时，内置了打包器、转译器、任务运行器和 npm 客户端
bunx=C:\Users\Administrator\.bun\bin\bunx.exe  # bunx 是 bun x 的别名
python=C:\Python3.11\python.exe

3.2.2 配置MCP服务端

采用json格式，一个MCP服务端一个json文件

3.2.2.1 stdio示例

weather.stdio.json

{
    "command": "uv",
    "args": [
        "--directory",
        "E:/Full/Path/McpServer/weather",
        "run",
        "weather.py"
    ],
    "env": {}
}

3.2.2.2 sse示例

test_xuanyuan.sse.json

{
    "url": "http://127.0.0.1:8000/sse",
    "headers": {}
}

3.2.2.3 websocket

4 运行指南

4.1 交互聊天

直接运行 TaskMcpClient 命令

TaskMcpClient

4.2 任务执行

直接运行 TaskMcpClient 命令

TaskMcpClient task

5 配置任务

Metadata

Release files for TaskMcpClient 0.1.53

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Source distribution for TaskMcpClient 0.1.53
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Uploaded via	`uv/0.6.14`

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