Azure OpenAI MCP Agent Demo on top of Log Analyzer workspace

Infrastructure

Obviously, that uses Azure infrastructure. You will need to create following resources (please, follow the sequence):

1. Azure OpenAI:
   1. Please, create chat completion deployment. GPT-4o is used in this case, but feel free to experiment with others.
2. Azure Log Analytics:
   1. Please, create Azure Log Analytics resource. Hint: after next steps, data will arrive into tables created under this resource. By default, data retention is set to 30 days, but you can easily modify it per table.
3. Azure Communication Services - Email:
   1. Please, create and setup email functionality with Azure Communication Services. This link can be a good start. Important: once setup, you need to have at least 3 following resources:
      1. Communication Service - your main Azure Communication Services resource
      2. Email Communication Service - ACS Email resource
      3. Email Communication Services Domain - Resource holding link between ACS Email resource and domain to be used for email sending
   2. Go to Diagnostic settings and click on Add diagnostic settings. Either check allLogs or select all email-related ones: Email Service Send Mail Logs, Email Service Delivery Status Update Logs, Email Service User Engagement Logs. In Destination details click Send to Log Analytics workspace and select your freshly created Log Analytics resource.
   3. Verify using Email -> Try Email that messages can be sent. Got to Monitoring -> Logs and try some email-related queries. Please, keep in mind that logs might arrive with 5 minute delay.
4. Microsoft Entra ID and App Identity:
   1. Search for a following resource: Microsoft Entra ID.
   2. In this resource, go to Manage -> App registrations and click on New registration. Give it a name and leave all settings by default.
   3. Click on newly created registration, go to Manage -> API permissions and select Add a permission. Go to APIs my organization uses and search for Log Analytics API. Then Delegated permissions. Select Data.Read checkbox and then Add permissions. Validate that your newly created app has this permission as described here.
   4. Go to Certificates & secrets and click on New client secret. Give it description and save value of secret generated.

Running locally

If you want to test it, you need two things:

1. Install docker
2. Create file, for example .demo.env and add the following content there:

AZURE_APP_CLIENT_ID=<Application (client) ID from application created in step 4; you may find it on resource Overview page>
AZURE_APP_CLIENT_SECRET=<Value of secret generated in step 4.4>
AZURE_APP_TENANT_ID=<Directory (tenant) ID from application created in step 4; you may find it on resource Overview page>
AZURE_LOG_ANALYTICS_WORKSPACE_ID=<Workspace ID of resource created in step 2; you may find it on resource Overview page>
AZURE_OPENAI_CHATGPT_DEPLOYMENT=<name of your chat gpt deployment>
AZURE_OPENAI_ENDPOINT=<endpoint of your Azure Open AI deployment>
AZURE_OPENAI_API_KEY=<key for using your Azure Open AI deployment>
AZURE_RESOURCE_ID=<resouce id created in step 1; you can copy it from the URL once you are in the resource, the format will be: /subscriptions/<subscription-id>/resourceGroups/<your-resource-group>/providers/Microsoft.Communication/CommunicationServices/<name-of-our-resource>
AZURE_CHAT_ENDPOINT=<Grab Endpoint from Settings -> Keys of your Communication Service created in step 3>
AZURE_CHAT_ACCESS_KEY=<Grab Key from Settings -> Keys of your Communication Service created in step 3>

Then you can do:

docker compose -f docker-compose.yml --env-file .demo.env build
docker compose -f docker-compose.yml --env-file .demo.env up -d

Application becomes available at http://localhost. Enjoy!

Developing locally

Backend

As code is written in C#, use Visual Studio (Community Edition is more than enough). Prior running it, you will need to setup the same variables mentioned for Running locally section, but as environment variables. Then run it as http. It should open with Swagger on port 5247.

Frontend

As this part is heavy on TypeScript (React, Redux, Ant Design), I recommend using Visual Studio Code. Depending on port used for Backend instance, you might want to adjust BACKEND_HOST_URL in ./build-configuration/dev.properties file. First, you need to do:

yarn

Then start it with:

yarn start

Application becomes available at http://localhost:8080.