What tools does the server expose?
It exposes five tools: `file_system_create_directory`, `file_system_write_file`, `file_system_read_file`, `file_system_list_directory`, and `execute_shell_command`.
Python MCP Server Project
@donohue76
Basic information
About Python MCP Server Project
Model Context Protocol server
Python MCP Server Project config
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryPython MCP Server Project tools
Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is Python MCP Server Project?
This project implements a Model Context Protocol (MCP) server using Python. It exposes local capabilities like file system access and command execution to MCP clients such as AI agents or language models, and is fully containerized with Docker.
How to use Python MCP Server Project?
Build the Docker image with docker build -t mcp-server ., then run the container with docker run -d ... providing environment variables and volume mounts. Use the /health endpoint or the provided Python test client to verify the server.
Key features of Python MCP Server Project
- File system directory creation and listing.
- File writing and reading.
- Shell command execution inside the container.
- Fully containerized with Docker for easy deployment.
- Configurable via environment variables (port, base directory, shell commands toggle).
Use cases of Python MCP Server Project
- Allowing AI agents to create, read, and write files on a host machine.
- Enabling language models to execute shell commands within a controlled container.
- Providing structured file system access for MCP-based tools and workflows.
Frequently asked questions
How do I enable or disable shell command execution?
Set the environment variable `ALLOW_ARBITRARY_SHELL_COMMANDS` to `"true"` (enabled) or `"false"` (disabled) when running the Docker container.
What are the runtime requirements?
The server requires Docker Desktop with WSL2 integration (on Windows) or Docker on Linux. The client-side test script needs Python 3.12+ with `venv`.
How does data persistence work?
File system tools operate on a base working directory inside the container. You can mount a host directory into that path using a Docker volume mount to persist data on the host.
How is the server served?
The server uses Uvicorn as the ASGI server and Starlette as the ASGI framework, with the MCP server built using `FastMCP` from the `mcp` Python SDK.
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