About Python_mcp_server
Python_mcp_server is a short setup guide for installing Python packaging tools and the MCP CLI, then installing and testing an MCP project. It is aimed at users following the listed commands; the input does not describe server capabilities.
Key features
- 01Download and run get-pip.py
- 02Install mcp[cli]
- 03Install store_count_mcp.py with uv
- 04Test with MCP Inspector
- 05Install project dependencies with uv
Use cases
- 01Bootstrap pip in a Python environment
- 02Install the MCP CLI
- 03Install a local MCP script
- 04Test an MCP implementation with Inspector
MCP support
The instructions install the MCP CLI and show how to install a local MCP script. They do not describe what tools or resources that server exposes.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project README-
下載
get-pip.py: 你可以使用curl或wget從官方來源下載get-pip.py。curl -O [https://bootstrap.pypa.io/get-pip.py](https://bootstrap.pypa.io/get-pip.py) # 或 wget [https://bootstrap.pypa.io/get-pip.py](https://bootstrap.pypa.io/get-pip.py) -
使用
uv執行get-pip.py: 切換到你下載get-pip.py的目錄,然後執行以下命令:uv run python get-pip.py這個命令會指示
uv使用 Python 解釋器來執行get-pip.py腳本,從而下載並安裝pip及其相關依賴(如setuptools和wheel)。 -
安裝 mcp[cli]:
pip install mcp[cli] -
使用 uv 執行 mcp 安裝 sotre_count_mcp.py:
uv run mcp install store_count_mcp.py這會自動安裝到有 mcp 設定的地方。
-
** mcp inspector 可測試 **
npx @modelcontextprotocol/inspector -
** 專案初始 **
uv pip install -e . 通常,在一個專案的工作流程中,您會先使用 uv pip compile pyproject.toml -o uv.lock 來生成或更新 uv.lock 檔案, 然後再使用 uv pip sync uv.lock(基於 uv.lock)或 uv pip install -e . 來安裝專案及其依賴。
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.