Integration

Azure OpenAI integration

Azure OpenAI Service API for accessing OpenAI models hosted on Microsoft Azure. Create chat completions, generate embeddings, create images, manage deployments, and fine-tune models with enterprise-grade security and compliance.

Connect Azure OpenAI How integrations work

Ask it in chat

Say what should happen.
The workflow builds itself.

When this happens, do that in Azure OpenAI.Built from the 24 Azure OpenAI tools below, on the connection you add once.

Common Azure OpenAI use cases

automate file management
automate batch management
automate fine tuning job management
automate model management
get file programmatically
get batch programmatically
get model programmatically
list file programmatically
list batch programmatically
list model programmatically

What you can do with Azure OpenAI

Each of these is one operation on one endpoint, available both as a workflow step and as a tool an AI agent can call on its own. Nothing bundles several operations into one action, so what a run did stays readable afterwards.

Most endpoints run on the free plan. A few, mainly the ones that publish on your behalf, need a paid plan.

Batch, 4 operations

cancel_batchCancel batchPOSTCancel an in-progress batch job. The batch will transition to a cancelling state and stop processing further requests.
create_batchCreate batchPOSTCreate a batch processing job to run multiple requests asynchronously at lower cost. Submit an input JSONL file containing requests and receive results when the batch completes.
get_batchGet batchGETGet the status and details of a batch job including its progress, request counts, and output file IDs.
list_batchesList batchesGETList all batch jobs in the Azure OpenAI resource.

File, 4 operations

delete_fileDelete fileDELETEDelete an uploaded file from the Azure OpenAI resource.
get_fileGet fileGETGet metadata for a specific uploaded file including its name, size, purpose, and status.
list_filesList filesGETList all files that have been uploaded to the Azure OpenAI resource. Returns file IDs, names, sizes, purposes, and statuses.
upload_fileUpload filePOSTUpload a file to be used with fine-tuning or other operations. Files must be in JSONL format for fine-tuning. Maximum file size is 512 MB.

Fine tuning job, 4 operations

cancel_fine_tuning_jobCancel fine tuning jobPOSTCancel an in-progress fine-tuning job. The job will transition to a cancelled state and stop incurring charges.
create_fine_tuning_jobCreate fine tuning jobPOSTCreate a fine-tuning job to customize a model with your training data. Specify the base model, training file, and optional hyperparameters.
get_fine_tuning_jobGet fine tuning jobGETGet details of a specific fine-tuning job including its status, model, training file, hyperparameters, and result files.
list_fine_tuning_jobsList fine tuning jobsGETList all fine-tuning jobs in the Azure OpenAI resource. Returns job IDs, statuses, base models, training files, and hyperparameters.

Model, 2 operations

get_modelGet modelGETGet details of a specific model including its capabilities, lifecycle status, deprecation info, and supported features.
list_modelsList modelsGETList all available models in the Azure OpenAI resource. Returns model IDs, capabilities, deprecation dates, and fine-tuning availability.

Chat completion, 1 operation

create_chat_completionCreate chat completionPOSTSend a conversation to an Azure-hosted OpenAI model and receive an intelligent response. Supports multi-turn conversations, function/tool calling, streaming, JSON mode, and customizable generation parameters.

Completion, 1 operation

create_completionCreate completionPOSTGenerate text completions from a given prompt using an Azure-hosted OpenAI model. Supports customizable generation parameters for controlling output length, randomness, and stopping behavior.

Embedding, 1 operation

create_embeddingsCreate embeddingsPOSTGenerate vector embeddings for input text using an Azure-hosted OpenAI embeddings model. Useful for semantic search, clustering, classification, and similarity comparisons.

File content, 1 operation

download_file_contentDownload file contentGETDownload the raw content of an uploaded file.

Fine tuning event, 1 operation

list_fine_tuning_eventsList fine tuning eventsGETList events for a fine-tuning job to track its progress, training metrics, and status changes.

Image, 1 operation

create_imageCreate imagePOSTGenerate images from a text prompt using an Azure-hosted DALL-E model. Supports different sizes, quality levels, and artistic styles.

Image edit, 1 operation

create_image_editCreate image editPOSTEdit or extend an image using a DALL-E model. Provide the source image and a text prompt describing the desired edit, optionally with a mask defining the region to modify.

Speech, 1 operation

create_speechCreate speechPOSTGenerate spoken audio from input text using an Azure-hosted TTS model. Supports multiple voices and output formats.

Transcription, 1 operation

create_transcriptionCreate transcriptionPOSTTranscribe audio into text using an Azure-hosted Whisper model. Supports multiple audio formats and languages.

Translation, 1 operation

create_translationCreate translationPOSTTranslate audio into English text using an Azure-hosted Whisper model. The input audio can be in any supported language.

Use Azure OpenAI from Claude Code, Cursor or Codex

Connect Azure OpenAI in LiveContext, create an API key in Settings, then add the LiveContext MCP server to your client. Its Azure OpenAI operations become tools the client can call, next to every other integration you connected.

Claude Code

claude mcp add --transport http livecontext https://livecontext.ai/mcp --header "X-API-Key: YOUR_API_KEY"

Codex (~/.codex/config.toml)

[mcp_servers.livecontext]
url = "https://livecontext.ai/mcp"

[mcp_servers.livecontext.http_headers]
"X-API-Key" = "YOUR_API_KEY"

Cursor, Claude Desktop and other JSON configs

{
  "mcpServers": {
    "livecontext": {
      "type": "http",
      "url": "https://livecontext.ai/mcp",
      "headers": {
        "X-API-Key": "YOUR_API_KEY"
      }
    }
  }
}

Set up the MCP server step by step

How to connect Azure OpenAI

Create your workspace
Sign up free. Nothing to install.
Connect Azure OpenAI once
Add your Azure OpenAI connection in Settings. Every tool from this integration reuses it, and secrets are encrypted at rest and visible only to you.
Describe the job in chat
The workflow builds itself in front of you and picks the endpoints it needs. Drag the steps yourself on the canvas whenever you would rather be explicit.

Azure OpenAI integration FAQ

What can you do with the Azure OpenAI integration?

LiveContext exposes 24 operations on Azure OpenAI, covering batch, file, fine tuning job and model. Common uses include automate file management, automate batch management and automate fine tuning job management.