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.

LiveContext

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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.

24 toolsAPI keyAPI docs

Connect Azure OpenAI How integrations work

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

POSTcancel_batch
Cancel an in-progress batch job. The batch will transition to a cancelling state and stop processing further requests.
POSTcreate_batch
Create 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.
GETget_batch
Get the status and details of a batch job including its progress, request counts, and output file IDs.
GETlist_batches
List all batch jobs in the Azure OpenAI resource.

File, 4 operations

DELETEdelete_file
Delete an uploaded file from the Azure OpenAI resource.
GETget_file
Get metadata for a specific uploaded file including its name, size, purpose, and status.
GETlist_files
List all files that have been uploaded to the Azure OpenAI resource. Returns file IDs, names, sizes, purposes, and statuses.
POSTupload_file
Upload 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

POSTcancel_fine_tuning_job
Cancel an in-progress fine-tuning job. The job will transition to a cancelled state and stop incurring charges.
POSTcreate_fine_tuning_job
Create a fine-tuning job to customize a model with your training data. Specify the base model, training file, and optional hyperparameters.
GETget_fine_tuning_job
Get details of a specific fine-tuning job including its status, model, training file, hyperparameters, and result files.
GETlist_fine_tuning_jobs
List all fine-tuning jobs in the Azure OpenAI resource. Returns job IDs, statuses, base models, training files, and hyperparameters.

Model, 2 operations

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

Chat completion, 1 operation

POSTcreate_chat_completion
Send 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

POSTcreate_completion
Generate 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

POSTcreate_embeddings
Generate 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

GETdownload_file_content
Download the raw content of an uploaded file.

Fine tuning event, 1 operation

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

Image, 1 operation

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

Image edit, 1 operation

POSTcreate_image_edit
Edit 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

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

Transcription, 1 operation

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

Translation, 1 operation

POSTcreate_translation
Translate 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.