Azure AI Vision Face API MCP Server

Introducing a Face Detection and Recognition MCP Server to allow the embedding of face attribute detection and face recognition during Agentic AI workflows.

https://github.com/user-attachments/assets/dac4ef24-2043-47a2-8858-c965970254f9

Face Detection and Recognition API

For more information, visit Face API

Running MCP Server

Installation

1. Using UV

Refer to the Installation Guide for instructions on installing uv.

2. Clone this project

We provide several example images in the example folder. Please clone this project to try the example prompts below.

git clone https://github.com/Azure-Samples/azure-ai-vision-face-api-mcp-server.git

3. Set Up Azure AI Vision Face API

• Go to the Azure Portal and create a new Face resource.
• After deployment, navigate to the resource and copy the Endpoint URL and one of the Key from the "Keys and Endpoint" section.
• You will be prompted to enter the following environment variables the first time you start the MCP server:
  • Azure AI Face API Endpoint: The endpoint URL of your Azure Face API deployment.
  • Azure AI Face API Key: The API key for your Azure Face API resource.

4. (Optional) Deploy GPT-4.1 on Azure OpenAI for Open-Set Attribute Detection

• To enable open-set face attribute detection, you need access to Azure OpenAI.
• In the Azure Portal, create an Azure OpenAI resource and deploy a GPT-4.1 model.
• Once deployed, obtain the Endpoint URL and API Key for your OpenAI resource.
• You will be prompted to enter the following environment variables the first time you start the MCP server:
  • Azure OpenAI Endpoint: The endpoint URL of your Azure OpenAI deployment.
  • Azure OpenAI API Key: The API key for your Azure OpenAI resource.
• For more details about using , see the Azure OpenAI documentation.

5. (Optional) Configure Azure Storage for Image Management

• To enable image management and access, you need an Azure Storage account.
• In the Azure Portal, create an Azure Storage resource and set up a container for your images.
• Once created, obtain your Storage Account Name, Container Name, and a SAS Token for secure access.
• You will be prompted to enter the following environment variables the first time you start the MCP server:
  • AZURE STORAGE ACCOUNT: The name of your Azure Storage account.
  • AZURE STORAGE CONTAINER: The name of your image container.
  • AZURE STORAGE SAS TOKEN: The SAS token for your storage container.
• For more details about using Azure Storage, see the Azure Storage documentation.

6. Interact with our MCP tools using Visual Studio Code GitHub Copilot

• Install GitHub Copilot Visual Studio Code extension.
• Rename the file .vscode/mcp-bp.json to .vscode/mcp.json (or copy .vscode/mcp-bp.json to your existing .vscode/mcp.json) and press Start above the azure_ai_vision_face_api_mcp_server in .vscode/mcp.json.
• Follow this guide to add our MCP tools in the workspace and start a conversation with GitHub Copilot and use MCP tools in agent mode leveraging GPT-4.1.

7. (Optional) Prepare Environment for Local Testing

• Before running pytest or other scripts outside of MCP, copy .env.example to .env.
• Fill in your real keys in .env. These keys are the same as those used in .vscode/mcp.json.

8. (Optional) MCP HTTP Bridge (Node/TypeScript)

• This repo includes a small HTTP bridge in the bridge/ folder.
• The bridge launches the local Azure Face MCP server (via uvx --from …) using stdio and exposes a REST API for listing and calling MCP tools.
• It makes the MCP server usable from OpenAI Responses API or any web frontend.
• Available endpoints
  • GET /health → { ok: true }
  • GET /mcp/tools → list available MCP tools
  • POST /mcp/call → call a tool by name with arguments Usage

  # Setup
  cd bridge
  npm install
  cp .env.example .env   # fill in your environment values (see step 7 above)
  npm run dev
  
  # Examples:
  # list all MCP tools
  curl http://127.0.0.1:8787/mcp/tools
  # list all large person groups
  curl --% -X POST http://127.0.0.1:8787/mcp/call -H "Content-Type: application/json" -d "{ \"name\": \"azure_face_recognition_list_large_person_groups\", \"arguments\": {} }"
  

Example Prompts

• You may be prompted to agree to use the MCP tool the first time you use each MCP tool. Please press Continue to proceed.

Face Attribute Detection

1. Test the face attribute detection:

Check all the faces inside detection1.jpg wearing the mask or glasses

2. Test the open-set attribute detection:

Analyze the hair color and gender of all individuals in detection2.jpg

3. Test the attribute detection using image URL:

Check all the faces' age and gender inside https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/refs/heads/master/Face/images/detection4.jpg

Face Image Comparison

1. Compare the similarity between two face images:

Compare the identification1.jpg with https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/refs/heads/master/Face/images/findsimilar.jpg

2. Compare the similarity between one image and another image folder:

Compare test-image-person-group.jpg with all the images in reco/Bob" using the "most_similar" option

Face Recognition

1. Test the face enrollment and identify without handling any uuid by user:

create a new person group and enroll all the images in the example/reco folder and check example/test-image-person-group.jpg belongs to which person with which person id. Also check the following image belongs to which person: https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/refs/heads/master/Face/images/extra-woman-image.jpg