What is Azurecosmosdb Mcp Chat App

The azurecosmosdb-mcp-chat-app is a full-stack chat application that utilizes Azure Cosmos DB as a Model Context Provider (MCP) client, allowing for efficient data management and interaction within a chat environment.

Use cases

Use cases include building customer service chatbots, facilitating team communication in organizations, and creating interactive learning environments where users can engage in discussions.

How to use

To use the azurecosmosdb-mcp-chat-app, set up the environment variables in a .env file, then deploy the MCP server locally using Docker or to Azure using Azure Container Apps. Connect the MCP client via the provided SSE endpoint.

Key features

Key features include integration with Azure Cosmos DB for NoSQL data storage, support for interactive chat UIs using Chainlit and Gradio, and the use of Managed Identity for secure authentication.

Where to use

The azurecosmosdb-mcp-chat-app can be used in various fields such as customer support, team collaboration, and any application requiring real-time chat functionalities.

🧠 Python MCP Server & Client Example with Azure Cosmos DB

Overview

This repository contains a complete example of a chat application powered by a Model Context Protocol (MCP) server written in Python. It demonstrates how to:

• Build and run an MCP Server that interacts with Azure Cosmos DB for NoSQL
• Use chainlit and gradio as MCP clients to build interactive chat UIs
• Host both server and client locally or deploy them to Azure

The MCP server exposes tools that let LLM agents insert and query data from Azure Cosmos DB. It uses Managed Identity for authentication and assumes the use of Azure OpenAI for embeddings and LLM responses (you can modify it to use other providers as needed).

In action with VS Code

https://github.com/user-attachments/assets/e3857c4b-7961-4eaf-b211-7ceb2d4006b6

🛠️ Project Structure

azure_containers/cosmosdb/   # MCP Server using Azure Container Apps
azure_functions/cosmosdb/    # MCP Server using Azure Functions
mcp_client/                  # MCP Client using Chainlit or Gradio

🚀 Deploying the MCP Server

The MCP server exposes an SSE (Server-Sent Events) endpoint that the client connects to. You can run it locally with Docker or deploy it to Azure using Azure Container Apps or Functions.

1. Set up environment variables

Create a .env file in the root of your server folder with:

ACCOUNT_KEY=<Your Azure Cosmos DB Account Key>
ACCOUNT_ENDPOINT=<Your Azure Cosmos DB Account Endpoint>

2. Local Deployment (with Docker)

Run the following commands:

docker-compose build
docker-compose up -d

Once running, the MCP Server is available at http://localhost:5000/

3. Azure Deployment (Container Apps)

You can deploy via Azure Container Apps using the VS Code extension. Before that:

• Create an Azure Container App in your subscription.
• Then deploy via VS Code.

Update your .env with the following GPT model details:

openai_key=<API key for Azure OpenAI model>
openai_endpoint=<Base URL of your Azure OpenAI deployment>
openai_api_version=2025-01-01-preview
openai_embeddings_deployment="text-embedding-3-large-2"
openai_embeddings_model="text-embedding-3-large"

💬 Deploying the MCP Client

The MCP client is located in the mcp_client/ folder. It uses either chainlit or gradio to create a front-end chat UI connected to the MCP server.

1. Set up environment variables

Same .env as above:

CHAT_MODEL_NAME=<Your GPT deployment name>
CHAT_MODEL_API_KEY=<API key for Azure OpenAI model>
CHAT_MODEL_BASE_URL=<Base URL of your Azure OpenAI deployment>
CHAT_MODEL_API_VERSION=2025-01-01-preview

2. Local Deployment

Install dependencies:

pip install -r requirements.txt

Then run either interface:

Chainlit:

python -m chainlit run chat_app.py

Gradio:

python app.py

The app runs locally at: http://localhost:8000/

💡 Once opened, use the UI plug-in button to set your MCP server SSE endpoint (e.g., http://localhost:5000/sse or your Azure-deployed server).

3. Azure Deployment (App Service)

To deploy to Azure App Service:

• Use the Azure App Service VS Code extension.
• Create an App Service resource in your Azure subscription.
• Deploy the code from mcp_client/.

🔧 Notes & Customization

• This sample assumes usage of Azure OpenAI for embeddings and LLMs. To use other providers (e.g., OpenAI, HuggingFace), adjust the API calls accordingly.
• The MCP Server supports Cosmos DB for NoSQL only, but you can extend it to other APIs if needed.

📎 Related Technologies

• Azure Cosmos DB
• Azure Container Apps
• Azure App Service
• Chainlit
• Gradio

📬 Feedback

We welcome contributions and feedback! Please open issues or PRs if you’d like to improve the project or have suggestions.