MCP on Azure
Generates descriptions for images stored in Azure Blob Storage using Azure OpenAI's GPT-4o vision model.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP on Azurelist all blobs in the container"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP on Azure β Model Context Server that reads from Blob Storage and describes images with Azure OpenAI
Production-ready sample for running a minimal MCP Server on Azure. It exposes two tools:
list_captain_filesβ list all blob names in a containerdescribe_blobβ generate a short description for an image blob using Azure OpenAI (gpt-4o)
β¨ What you get
A minimal MCP Server built with Express.js.
Secure access to Azure Blob Storage using a connection string.
Server-side calls to Azure OpenAI vision models for image description.
Example
.env.examplefor easy setup.PowerShell & cURL commands for quick testing.
Related MCP server: Vision MCP
π§± Architecture
flowchart TD
A[MCP Client] -->|HTTP| B[MCP Server]
B -->|List/Fetch| C[Azure Blob Storage]
B -->|Send image| D[Azure OpenAI gpt-4o Vision]
D -->|Description| B
B -->|JSON Response| A
MCP Client (Copilot, Inspector, etc.) sends HTTP requests.
This Server (Express.js) handles /tools endpoints.
Server fetches the file from Azure Blob Storage.
Sends it to Azure OpenAI GPT-4o for a short, clear description.
π¦ Prerequisites
Node.js 18+ (or 20/22)
Azure Storage Account with a container (e.g.,
captain-azure) containing imagesAzure OpenAI resource with a gpt-4o deployment
Access keys & endpoint for both services
π Environment Variables (.env)
AZURE_STORAGE_CONNECTION_STRING=DefaultEndpointsProtocol=https;AccountName=<name>;AccountKey=<key>;EndpointSuffix=core.windows.net
CONTAINER_NAME=captain-azure
AZURE_OPENAI_ENDPOINT=https://<your-openai>.openai.azure.com
AZURE_OPENAI_KEY=<your-openai-key>
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o
AZURE_OPENAI_API_VERSION=2024-02-15-previewπ Install & Run
git clone https://github.com/OfekBenEliezer/mcp-on-azure.git
cd mcp-on-azure
npm install
node server.js
# MCP Server running on: http://localhost:3333π List blobs
PowerShell
Invoke-RestMethod -Uri "http://localhost:3333/tools/list_captain_files" `
-Method Post `
-ContentType "application/json"cURL
curl -s -X POST http://localhost:3333/tools/list_captain_files \
-H "Content-Type: application/json"πΌ Describe a blob
PowerShell
Invoke-RestMethod -Uri "http://localhost:3333/tools/describe_blob" `
-Method Post `
-Body (@{ blobName = "CertWebinar.png" } | ConvertTo-Json) `
-ContentType "application/json"cURL
curl -s -X POST http://localhost:3333/tools/describe_blob \
-H "Content-Type: application/json" \
-d '{"blobName":"CertWebinar.png"}'Expected output
{
"description": "A short, clear sentence describing the image."
}π‘οΈ Security Notes
Never commit keys or secrets.
If blobs are private, generate SAS URLs with short TTL.
Restrict CORS and ingress when running in production.
For enterprise scenarios, prefer Managed Identity over raw keys.
π License
MIT License β free to use, modify, and distribute with attribution.
π¨ββοΈ Built by Captain Azure β Ofek Ben Eliezer
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