mcd-demo
About
Testing creation of simple MCP servers and integrating with LangChain agent
Details
- Author
- jspoelstra
- Downloads
- 202
- Categories
- AI
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- Demonstrates creation of simple MCP servers
- Integrates MCP servers with a LangChain agent
- Includes weather, math, and telemetry server examples
- Supports running math server in a Docker container
- Uses SSE transport for MCP server communication
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
mcd-demoCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Set up a Python virtual environment, install dependencies from requirements.txt, and configure environment variables for Azure OpenAI API key and endpoint. Then start the three MCP servers (weather_server.py, math_server.py, telemetry_server.py) in the background, optionally running the math server via Docker, and finally launch the agent with python agent.py.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcd-demo": {
"mcd-demo": {
"command": "python3",
"args": [
"-m",
"venv",
"venv"
]
}
}
}
}McpServers
{
"mcd-demo": {
"command": "python3",
"args": [
"-m",
"venv",
"venv"
]
}
}mcd-demo
Testing creation of simple MCP servers and integrating with LangChain agentPrerequisites
Create a virtual environment
python3 -m venv venv
source venv/bin/activate
Install dependencies
pip install -r requirements.txt
Set Environment variables
Set the following environment variables. Get the values from Azure AI Foundry where the models are deployed:
export AZURE_OPENAI_API_KEY=<your_azure_openai_api_key>
export AZURE_OPENAI_ENDPOINT=<your_azure_openai_endpoint>
Optionally, you can set the following environment variables to configure the MCP servers:
export MCP_MATH_URI=http://<server-uri>:5001/sse
> Note: You can also set these variables in a .env file in the root directory of the project.
Running the agent
Start the MCP servers
You have to start all three MCP servers before starting the agent. Each server listens on a separate port. You can start them in separate terminals or run them in the background. To run in the background, do the following:python weather_server.py &
python math_server.py &
python telemetry_server.py &
Alternatively, you can run the math server in a Docker container. To do this, first build the Docker image:
make build
Then, run the container:
make run-local
If you want to push the Docker image to a registry, tag and push it using the following commands:
make login
make push
Start the agent
python agent.py
Killing the MCP servers
pkill -9 -f weather_server.py
pkill -9 -f math_server.py
pkill -9 -f telemetry_server.py
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