lkr cli

The lkr cli is a tool for interacting with Looker. It combines Looker's SDK and customer logic to interact with Looker in meaninful ways. For a full list of commands, see the full cli docs

Usage

uv makes everyone's life easier. Go install it. You can start using lkr by running uv run --with lkr-dev-cli[all] lkr --help.

Alternatively, you can install lkr with pip install lkr-dev-cli[all] and use commands directly like lkr <command>.

We also have a public docker image that you can use to run lkr commands.

docker run -it --rm us-central1-docker.pkg.dev/lkr-dev-production/lkr-cli/cli:latest lkr --help

Login

Using OAuth2

See the prerequisites section

Login to lkr

uv run --with lkr-dev-cli[all] lkr auth login

• Select a new instance
• Put the url of your Looker instance (e.g. https://acme.cloud.looker.com)
• Choose whether you want this login to use production or development mode
• Give it a name

You will be redirected to the Looker OAuth authorization page, click Allow. If you do not see an allow button, the prerequisites were not done properly.

If everything is successful, you will see Successfully authenticated!. Test it with

uv run --with lkr-dev-cli[all] lkr auth whoami

Using API Key

If you provide environment variables for LOOKERSDK_CLIENT_ID, LOOKERSDK_CLIENT_SECRET, and LOOKERSDK_BASE_URL, lkr will use the API key to authenticate and the commands. We also support command line arguments to pass in the client id, client secret, and base url.

uv run --with lkr-dev-cli[all]  lkr --client-id <your client id> --client-secret <your client secret> --base-url <your instance url> auth whoami

OAuth2 Prerequisites

If this if the first time you're using the Language Server, you'll need to register a new OAuth client to communicate with lkr cli.

lkr uses OAuth2 to authenticate to Looker and manages the authentication lifecycle for you. A Looker Admin will need to Register a new OAuth client to communicate with the Language Server:

Go to the Looker API Explorer for Register OAuth App (https://your.looker.instance/extensions/marketplace_extension_api_explorer::api-explorer/4.0/methods/Auth/register_oauth_client_app)

• Enter lkr-cli as the client_id
• Enter the following payload in the body

{
  "redirect_uri": "http://localhost:8000/callback",
  "display_name": "LKR",
  "description": "lkr.dev language server, MCP and CLI",
  "enabled": true
}

• Check the "I Understand" box and click the Run button
• This only needs to be done once per instance

MCP

Built into the lkr is an MCP server. Right now its tools are based on helping you work within an IDE. To use it a tool like Cursor, add this to your mcp.json

{
  "mcpServers": {
    "lkr-mcp": {
      "command": "uv",
      "args": ["run", "--with", "lkr-dev-cli[all]", "lkr", "mcp", "run"]
    },
    "lkr-mcp-docker": {
      "command": "docker",
      "args": ["run", "--rm", "-it", "us-central1-docker.pkg.dev/lkr-dev-production/lkr-cli/cli:latest", "lkr", "mcp", "run"]
    }
  }
}

Observability

The observability command provides tools for monitoring and interacting with Looker dashboard embeds. lkr observability embed will start a server that has endpoints for logging events from embedded dashboards. This tool is useful for monitoring the health of Looker embeds and your query times. It will spin up a chromium browser and use selinium and a simple HTML page to capture Looker's Javascript events

1. Embed user login times
2. Time to paint the dashboard dashboard:loaded including the login time
3. Time to finish running the dashboard dashboard:run:complete
4. Time for tiles to finish loading dashboard:tile:start and dashboard:tile:complete which is a proxy for your database query times

Note

There are other ways in Looker's System Activity to view query times and dashboard load performance, but System Activity is not recommended to poll this aggresively if you are not on Elite System Activity. If you are on Elite System Activity, the data feeds for system activity is too slow for health checks. Also, Looker's system activity doesn't have a way to tie both the dashboard load and the query times together; this tool is the best proxy for both as we collect them together all within a single embedded dashboard load session.

Primary Endpoint

• GET /health: Launches a headless browser to simulate embedding a dashboard, waits for a completion indicator, and logs the process for health checking. This endpoint accepts query parameters to help login users with custom attributes.

Important

Make sure you add the http://host:port to your domain allowlist in Admin Embed. docs Unless overridden, the default would be http://0.0.0.0:8080. These can also set via cli arguments. E.g., lkr observability embed --host localhost --port 7777 or by setting the environment variables HOST and PORT. You can check the embed_domain by sending a request to the /settings endpoint.

For example:

• dashboard_id: string required - The id of the dashboard to embed
• external_user_id: string required - The external_user_id of the user to login. We recommend not logging in as a known user, rather a standalone healthcheck user.
• group_ids: list[string] - The ids of the groups the user belongs to. Accepts multiple values like &group_ids=123&group_ids=456
• permissions: list[string] - The permissions the user has, defaults to access_data, see_user_dashboards, see_lookml_dashboards, see_looks, explore. Accepts multiple values like &permissions=access_data&permissions=see_user_dashboards
• models: list[string] required - The models the user has access to. Accepts multiple values like &models=acme_model1&models=acme_model2
• session_length: int - The length of the session. Defaults to 10 minutes.
• first_name: string - The first name of the user
• last_name: string - The last name of the user
• user_timezone: string - The timezone of the user
• user_attributes: string - The attributes of the user, this should be a stringified JSON like &user_attributes={"store_id": "123"}

Other Endpoints

• POST /log_event: Receives and logs events from embedded dashboards. The embe
• GET /settings: Returns the current embed configuration and checks if the requesting domain is allowed. Useful for debugging why you're not receiving events
• GET /: Serves a static HTML file for embedding.

Logging Events

lkr observability embed will have structured logs to stdout as well as return a JSON object with the events at the end of the request. These can be turned off with the --quiet flag. lkr --quiet observability embed will not print anything to stdout but still return the logged events in the response body.

• lkr-observability:health_check_start: The API has started
• lkr-observability:health_check_timeout: The API has timed out
• lkr-observability:health_check_error: The API has failed wuth an error
• lkr-observability:dashboard:loaded: The dashboard has loaded, based on Looker's dashboard:loaded event
• lkr-observability:dashboard:run:start: The dashboard has run started, based on Looker's dashboard:run:start event
• lkr-observability:dashboard:run:complete: The dashboard has run complete, based on Looker's dashboard:run:complete event
• lkr-observability:dashboard:tile:start: The dashboard tile has started, based on Looker's dashboard:tile:start event
• lkr-observability:dashboard:tile:complete: The dashboard tile has completed, based on Looker's dashboard:tile:complete event

Payload

• event_type: The type of event
• event_at: The time the event occurred
• time_since_start: The time since the session started
• payload: Additional data that may have been returned from the Javascript event
• session_id: The session id, corresponds to a single API request to GET /health.
• last_event_type: The type of the last event if there is one
• last_event_at: The time the last event occurred, if there is one
• time_since_last_event: The time since the last event, if there is one
• external_user_id: The external user id of the user who is running the dashboard
• dashboard_id: The id of the dashboard that was run

Cloud Run + GCP Health Check example

One of the simplest ways to launch the health check is the lkr-cli public docker image, Cloud Run, and the GCP health check service. Here's an example; make sure to change your region and project. HEALTH_URL is an example of how to structure the query parameters for the health check.

export REGION=<your region>
export PROJECT=<your project id>

export HEALTH_URL="/health?dashboard_id=1&external_user_id=observability-embed-user&models=thelook&user_attributes={\"store_id\":\"1\"}"

gcloud run deploy lkr-observability \
  --image us-central1-docker.pkg.dev/lkr-dev-production/lkr-cli/cli:latest \
  --command lkr \
  --args observability,embed \
  --platform managed \
  --region $REGION \
  --project $PROJECT \
  --cpu 2 \
  --memory 4Gi \
  --set-env-vars LOOKERSDK_CLIENT_ID=<your client id>,LOOKERSDK_CLIENT_SECRET=<your client secret>,LOOKERSDK_BASE_URL=<your instance url> 

gcloud monitoring uptime create lkr-observability-health-check \
  --protocol https \
  --project $PROJECT \
  --resource-type="cloud-run-revision" \
  --resource-labels="project_id=${PROJECT},service_name=lkr-observability,location=${REGION}" \
  --path="${HEALTH_URL}" \
  --period="15" \
  --timeout="60"

Alternative Usage

This can also be used to stress test your Looker environment as it serves an API that logs into a Looker embedded dashboard and runs queries like a user would within Chromium. If you wrote a script to repeatedly call this API with different parameters, you could use it to stress test your Looker environment and/or your database.

User Attribute Updater (OIDC Token)

1. Create a new cloud run using the lkr-cli public docker image us-central1-docker.pkg.dev/lkr-dev-production/lkr-cli/cli:latest
2. Put in the environment variables LOOKERSDK_CLIENT_ID, LOOKERSDK_CLIENT_SECRET, LOOKERSDK_BASE_URL, LOOKER_WHITELISTED_BASE_URLS. The LOOKER_WHITELISTED_BASE_URLS would be the same url as the LOOKERSDK_BASE_URL if you are only using this for a single Looker instance. For more advanced use cases, you can set the LOOKER_WHITELISTED_BASE_URLS to a comma separated list of urls. The body of the request also accepts a base_url, client_id, and client_secret key that will override these settings. See example gcloud command
3. For the command and arguments use:
   • command: lkr
   • args: tools user-attribute-updater
4. Deploy the cloud run
5. Retrieve the URL of the cloud run
6. Create the user attribute
   • Name: cloud_run_access_token
   • Data Type: String
   • User Access: None
   • Hide values: Yes
   • Domain Allowlist: The URL of the cloud run from step 4. Looker will only allow the user attribute to be set if the request is going to this URL

Note

The user attribute name can be anything you want. Typically you will be using this with an extension, so you should follow the naming convention of the type of user attribute you will be using with the extension. I would recommend using a scoped user attributes for this. See Extension User Attributes. If you are using a global_user_attribute, then you can just use the name of it like cloud_run_access_token.

6. Create a new cloud scheduler

   • cron: 0 * * * *
   • Target Type: Cloud Run
   • URL: The URL of the cloud run from step 4 with a path of /identity_token. E.g. https://your-cloud-run-url.com/identity_token
   • HTTP Method: POST
   • Headers: Content-Type: application/json
   • Body: Use the user attribute_name from step 5, or use the user_attribute_id found in the Looker URL after you created it or are editing it

   {
     "user_attribute": "cloud_run_access_token",
     "update_type": "default"
   }

   • Auth Header: OIDC Token
   • Service Account: Choose the service account you want to use to run the cloud scheduler
   • Audience: The URL of the cloud run
   • Max Retries: >0

7. Make sure the Service Account has the Cloud Run Invoker role

8. Navigate the the cloud scheduler page, select the one you just created, and click Force Run

9. Check the logs of the cloud run to see if there was a 200 response

Example gcloud command

export REGION=<your region>
export PROJECT=<your project id>

gcloud run deploy lkr-access-token-updater \
  --image us-central1-docker.pkg.dev/lkr-dev-production/lkr-cli/cli:latest \
  --command lkr \
  --args tools,user-attribute-updater \
  --platform managed \
  --region $REGION \
  --project $PROJECT \
  --cpu 1 \
  --memory 2Gi \
  --set-env-vars LOOKERSDK_CLIENT_ID=<your client id>,LOOKERSDK_CLIENT_SECRET=<your client secret>,LOOKERSDK_BASE_URL=<your instance url>,LOOKER_WHITELISTED_BASE_URLS=<your instance url>

UserAttributeUpdater lkr-dev-cli

Exported from the lkr-dev-cli package is the UserAttributeUpdater pydantic class. This class has all the necessary logic to update a user attribute value.

It supports the following operations:

• Updating a default value
• Updating a group value
• Updating a user value
• Deleting a default value
• Deleting a group value
• Deleting a user value

It can also support looking up looker ids. It will lookup the following if the id is not provided:

• user_attribute_id by the name
• user_id by the email or external_user_id
• group_id by the name

Example Usage

from lkr import UserAttributeUpdater

# without credentials
updater = UserAttributeUpdater(
    user_attribute="cloud_run_access_token",
    update_type="default",
    value="123",
)


# with credentials
updater = UserAttributeUpdater(
    user_attribute="cloud_run_access_token",
    update_type="default",
    value="123",
    base_url="https://your-looker-instance.com",
    client_id="your-client-id",
    client_secret="your-client-secret",
)

updater.update_user_attribute_value()

# Getting authorization header from a FastAPI request
from fastapi import Request
from lkr import UserAttributeUpdater

@app.post("/request_authorization")
def request_authorization(request: Request):
    body = await request.json()
    updater = UserAttributeUpdater.model_validate(body)
    updater.get_request_authorization_for_value(request.headers.items())
    updater.update_user_attribute_value()

@app.post("/as_body")
def as_body(request: Request, body: UserAttributeUpdater):
    body.get_request_authorization_for_value(request.headers.items())
    body.update_user_attribute_value()

@app.post("/assigning_value")
def assigning_value(request: Request):
    updater = UserAttributeUpdater(
      user_attribute="cloud_run_access_token",
      update_type="default"
    )
    updater.value = request.headers.get("my_custom_header")
    updater.update_user_attribute_value()

@app.delete("/:user_attribute_name/:email")
def delete_user_attribute(user_attribute_name: str, email: str):
    updater = UserAttributeUpdater(
      user_attribute=user_attribute_name,
      update_type="user",
      email=email,
    )
    updater.delete_user_attribute_value()

## Optional Dependencies

The `lkr` CLI supports optional dependencies that enable additional functionality. You can install these individually or all at once.

### Available Extras

- **`mcp`**: Enables the MCP (Model Context Protocol) server functionality and `lkr mcp` commands
- **`observability`**: Enables the observability embed monitoring features and `lkr observability` commands
- **`tools`**: Enables the user attribute updater functionality and `lkr tools` commands

### Installing Optional Dependencies

**Install all optional dependencies:**
```bash
uv sync --extra all

Install specific extras:

# Install MCP functionality
uv sync --extra mcp

# Install observability features
uv sync --extra embed-observability

# Install user attribute updater
uv sync --extra user-attribute-updater

# Install multiple extras
uv sync --extra mcp --extra embed-observability

Using pip:

# Install all optional dependencies
pip install lkr-dev-cli[all]

# Install specific extras
pip install lkr-dev-cli[mcp,embed-observability,user-attribute-updater]