Get models

Returns one deployed inference model.

GET /v1/ml/models/{name}

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Address	`https://api.hanzo.ai/v1/ml/models/{name}`
Method	`GET`
Operation	`get_ml_models_by_name`
Auth	`Authorization: Bearer $HANZO_API_KEY`

Returns one deployed inference model. Its spec comes with it, and kserve's live status, which is where readiness and the serving address appear. A name the caller's org does not own answers 404, exactly as an unknown name does, so a probe learns nothing about another tenant's models.

Request

1 field.

Field	In	Type	Required	Description
`name`	path	string	yes	Name is the resource to act on, taken from the path.

Response

Status	Body	Meaning
`200`	ml.mlResource	ok
`default`	problem-details	refused

200 body — 6 fields.

Field	In	Type	Always	Description
`createdAt`	body	string	—	CreatedAt is when Kubernetes admitted the object, RFC 3339 in UTC.
`name`	body	string	—	Name is the object's metadata.name, unique within the caller's namespace.
`spec`	body	object	—	Spec is the resource spec, verbatim as Kubernetes stores it.
`spec.*`	body	any	—
`status`	body	object	—	Status is the live status kserve owns, verbatim.
`status.*`	body	any	—

Failure carries the platform error shape — see Errors.

Examples

CLI

SDK

HTTP

MCP

hanzo ml models get <name>

.ts

.py

.go

.rs

.java

import { Configuration, MlApi } from 'hanzoai';

const api = new MlApi(new Configuration({ accessToken: process.env.HANZO_API_KEY }));
const { data } = await api.getMlModelsByName({ name: 'name' });

from hanzoai.cloud import ApiClient, Configuration
from hanzoai.cloud.api import MlApi

client = ApiClient(Configuration(access_token=os.environ["HANZO_API_KEY"]))
result = MlApi(client).get_ml_models_by_name(name='name')

cfg := hanzoai.NewConfiguration()
cfg.AddDefaultHeader("Authorization", "Bearer "+os.Getenv("HANZO_API_KEY"))
client := hanzoai.NewAPIClient(cfg)

resp, _, err := client.MlAPI.GetMlModelsByName(context.Background()).Execute()
if err != nil {
	return err
}

use hanzo_client::apis::{configuration::Configuration, ml_api};

let mut cfg = Configuration::new();
cfg.bearer_access_token = std::env::var("HANZO_API_KEY").ok();

let result = ml_api::get_ml_models_by_name(&cfg, Default::default()).await?;

import ai.hanzo.cloud.ApiClient;
import ai.hanzo.cloud.api.MlApi;

ApiClient client = new ApiClient();
client.setBearerToken(System.getenv("HANZO_API_KEY"));

var result = new MlApi(client).getMlModelsByName();

curl https://api.hanzo.ai/v1/ml/models/<name> \
  -H "Authorization: Bearer $HANZO_API_KEY"

MCP reaches ml through the ml tool, which names its 4 operations with its own verbs — this one among them, under a name only MCP declares. describe explains any of them:

curl -X POST https://api.hanzo.ai/v1/mcp \
  -H "Content-Type: application/json" \
  -d '{
       "jsonrpc": "2.0",
       "id": 1,
       "method": "tools/call",
       "params": {
         "name": "describe",
         "arguments": {
           "op": "list_ml_models"
         }
       }
     }'

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