Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"simple-mcp-app": {
"args": [
"-y",
"@brightdata/mcp"
],
"command": "npx"
}
}
}Are you the author?
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What if your own language model could search the web and answer real-time questions? Just like ChatGPT, but running locally on your system, inside your app, where you have full control?
In this project, we’ll build exactly that using:
gemma3 on your local machine.Before running the app, make sure you have the following:
gemma3) pulled and serving. (check the video tutorial below for insturctions)export BRD_API_KEY=<your-key>
If you're not sure how to install the requirements, or what .bashrc is, or if you'd like to build this entire application step by step alongside me, please checkout my video tutorial on YouTube:
Get 10$ of Bright Data credits using the promo link (goes a long way!):
https://brdta.com/pythonsimplified_mcp
Just for perspective. I've featured Bright Data tools in a few tutorials already, and so far it cost me $6 bucks.
# Clone the repo
git clone https://github.com/MariyaSha/simple_mcp_app.git
cd simple_mcp_app
# Create working environment
conda create -n mcp_env python=3.12
conda activate mcp_env
conda install nodejs
# Make sure you've updated your .bashrc file (check Prerequisites)
npx @brightdata/mcp API_TOKEN=$BRD_API_KEY
# Install Python dependencies
pip install -r requirements.txt
cd simple_mcp_app
# OR cd advanced_mcp_app
# Run application
streamlit run mcp_app.py