What can you do with the Arize integration?

LiveContext exposes 32 operations on Arize, covering prompt, annotation config, dataset and experiment. Common uses include Fetch the production-labeled prompt for deployment, Remove a test project after validation is complete and Upload a curated Q&A golden set for LLM evaluation.

LiveContext

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Arize integration

AI observability and LLM monitoring platform. Manage spaces, projects, datasets, experiments, prompts, spans, and annotation configs for evaluating and monitoring AI/ML models. Features cursor-based pagination, structured JSON responses, and a CRUDL design pattern across all resources.

32 toolsTokenAPI docs

Connect Arize How integrations work

Common Arize use cases

Fetch the production-labeled prompt for deployment
Remove a test project after validation is complete
Upload a curated Q&A golden set for LLM evaluation
Verify a project exists before sending traces to it
Create a system prompt for a customer support chatbot
Discover all monitored AI applications in a workspace
Remove deprecated annotation schemas no longer in use
Set up a new project to monitor a chatbot application
Remove experimental prompts after a project is complete
Remove failed experiments that produced invalid results

What you can do with Arize

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.

Prompt, 5 operations

POSTcreate_prompt
Creates a new prompt with an initial version in an Arize space.
DELETEdelete_prompt
Permanently deletes an Arize prompt and all its versions by its ID.
GETget_prompt
Retrieves an Arize prompt by its ID with optional version or label.
GETlist_prompts
Lists all prompts the user has access to in Arize.
PATCHupdate_prompt
Updates an existing Arize prompt's description.

Annotation config, 4 operations

POSTcreate_annotation_config
Creates a new annotation configuration in an Arize space.
DELETEdelete_annotation_config
Permanently deletes an Arize annotation configuration by its ID.
GETget_annotation_config
Retrieves a single Arize annotation configuration by its ID.
GETlist_annotation_configs
Lists annotation configurations with optional filtering by space in Arize.

Dataset, 4 operations

POSTcreate_dataset
Creates a new dataset with examples in an Arize space.
DELETEdelete_dataset
Permanently deletes an Arize dataset by its ID.
GETget_dataset
Retrieves a single Arize dataset by its ID.
GETlist_datasets
Lists all datasets the user has access to in Arize.

Experiment, 4 operations

POSTcreate_experiment
Creates a new experiment with runs against an Arize dataset.
DELETEdelete_experiment
Permanently deletes an Arize experiment by its ID.
GETget_experiment
Retrieves a single Arize experiment by its ID.
GETlist_experiments
Lists experiments with optional filtering by dataset in Arize.

Project, 4 operations

POSTcreate_project
Creates a new project within an Arize space.
DELETEdelete_project
Permanently deletes an Arize project by its ID.
GETget_project
Retrieves a single Arize project by its ID.
GETlist_projects
Lists all projects the user has access to in Arize.

Space, 4 operations

POSTcreate_space
Creates a new space within an Arize organization.
GETget_space
Retrieves a single Arize space by its ID.
GETlist_spaces
Lists all spaces the user has access to in Arize.
PATCHupdate_space
Updates an existing Arize space's name or description.

Prompt version, 3 operations

POSTcreate_prompt_version
Creates a new version of an existing Arize prompt.
GETget_prompt_version
Retrieves a specific Arize prompt version by its version ID.
GETlist_prompt_versions
Lists versions for a specific Arize prompt.

Example, 2 operations

POSTadd_dataset_examples
Adds new examples to an existing Arize dataset, creating a new dataset version.
GETlist_dataset_examples
Lists examples for a specific Arize dataset.

Experiment run, 1 operation

GETlist_experiment_runs
Lists runs for a specific Arize experiment.

Span, 1 operation

POSTlist_spans
Lists spans filtered by project, time range, and optional filters in Arize.

Use Arize from Claude Code, Cursor or Codex

Connect Arize in LiveContext, create an API key in Settings, then add the LiveContext MCP server to your client. Its Arize 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 Arize

Create your workspace
Sign up free. Nothing to install.
Connect Arize once
Add your Arize 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.

Arize integration FAQ

What can you do with the Arize integration?: LiveContext exposes 32 operations on Arize, covering prompt, annotation config, dataset and experiment. Common uses include Fetch the production-labeled prompt for deployment, Remove a test project after validation is complete and Upload a curated Q&A golden set for LLM evaluation.