Analyze API (Beta): Use the Analyze API to analyze any external asset and return details based on the type of analysis requested.
Currently supports the following analysis options:
- AI Vision - Tagging
- AI Vision - Moderation
- AI Vision - General
- Captioning
- Cld Fashion
- Cld Text
- Coco
- Google Tagging
- Human Anatomy
- Image Quality Analysis
- Lvis
- Shop Classifier
- Unidet
- Watermark Detection
Notes:
- The Analyze API is currently in development and is available as a Public Beta, which means we value your feedback, so please feel free to share any thoughts with us.
- The analysis options require an active subscription to the relevant add-on. Learn more about registering for add-ons.
The API supports both Basic Authentication using your Cloudinary API Key and API Secret (which can be found on the Dashboard page of your Cloudinary Console) or OAuth2 (Contact support for more information regarding OAuth).
The SDK can be installed with either npm, pnpm, bun or yarn package managers.
npm add @cloudinary/analysis
pnpm add @cloudinary/analysis
bun add @cloudinary/analysis
yarn add @cloudinary/analysis zod
# Note that Yarn does not install peer dependencies automatically. You will need
# to install zod as shown above.
This SDK is also an installable MCP server where the various SDK methods are exposed as tools that can be invoked by AI applications.
Node.js v20 or greater is required to run the MCP server from npm.
Claude installation steps
Add the following server definition to your claude_desktop_config.json
file:
{
"mcpServers": {
"CloudinaryAnalysis": {
"command": "npx",
"args": [
"-y", "--package", "@cloudinary/analysis",
"--",
"mcp", "start",
"--cloud-name", "...",
"--api-key", "...",
"--api-secret", "..."
]
}
}
}
Cursor installation steps
Create a .cursor/mcp.json
file in your project root with the following content:
{
"mcpServers": {
"CloudinaryAnalysis": {
"command": "npx",
"args": [
"-y", "--package", "@cloudinary/analysis",
"--",
"mcp", "start",
"--cloud-name", "...",
"--api-key", "...",
"--api-secret", "..."
]
}
}
}
You can also run MCP servers as a standalone binary with no additional dependencies. You must pull these binaries from available Github releases:
curl -L -o mcp-server \
https://github.com/cloudinary/analysis-js/releases/download/{tag}/mcp-server-bun-darwin-arm64 && \
chmod +x mcp-server
For a full list of server arguments, run:
npx -y --package @cloudinary/analysis -- mcp start --help
For supported JavaScript runtimes, please consult RUNTIMES.md.
import { CloudinaryAnalysis } from "@cloudinary/analysis";
const cloudinaryAnalysis = new CloudinaryAnalysis({
security: {
cloudinaryAuth: {
apiKey: "CLOUDINARY_API_KEY",
apiSecret: "CLOUDINARY_API_SECRET",
},
},
});
async function run() {
const result = await cloudinaryAnalysis.analyze.aiVisionGeneral({
source: {
uri: "https://res.cloudinary.com/demo/image/upload/sample.jpg",
},
prompts: [
"Describe this image in detail",
"Does this image contain an insect?",
],
});
console.log(result);
}
run();
The default server https://api.cloudinary.com/v2/analysis/{cloud_name}
contains variables and is set to https://api.cloudinary.com/v2/analysis/CLOUD_NAME
by default. To override default values, the following parameters are available when initializing the SDK client instance:
Variable | Parameter | Default | Description |
---|---|---|---|
cloud_name |
cloudName: string |
"CLOUD_NAME" |
Your Cloud Name. |
import { CloudinaryAnalysis } from "@cloudinary/analysis";
const cloudinaryAnalysis = new CloudinaryAnalysis({
cloudName: "<value>",
security: {
cloudinaryAuth: {
apiKey: "CLOUDINARY_API_KEY",
apiSecret: "CLOUDINARY_API_SECRET",
},
},
});
async function run() {
const result = await cloudinaryAnalysis.analyze.aiVisionGeneral({
source: {
uri: "https://res.cloudinary.com/demo/image/upload/sample.jpg",
},
prompts: [
"Describe this image in detail",
"Does this image contain an insect?",
],
});
console.log(result);
}
run();
The default server can be overridden globally by passing a URL to the serverURL: string
optional parameter when initializing the SDK client instance. For example:
import { CloudinaryAnalysis } from "@cloudinary/analysis";
const cloudinaryAnalysis = new CloudinaryAnalysis({
serverURL: "https://api.cloudinary.com/v2/analysis/CLOUD_NAME",
security: {
cloudinaryAuth: {
apiKey: "CLOUDINARY_API_KEY",
apiSecret: "CLOUDINARY_API_SECRET",
},
},
});
async function run() {
const result = await cloudinaryAnalysis.analyze.aiVisionGeneral({
source: {
uri: "https://res.cloudinary.com/demo/image/upload/sample.jpg",
},
prompts: [
"Describe this image in detail",
"Does this image contain an insect?",
],
});
console.log(result);
}
run();
This SDK supports the following security schemes globally:
Name | Type | Scheme | Environment Variable |
---|---|---|---|
cloudinaryAuth |
http | Custom HTTP | CLOUDINARY_CLOUDINARY_AUTH |
oAuth2 |
oauth2 | OAuth2 token | CLOUDINARY_O_AUTH2 |
You can set the security parameters through the security
optional parameter when initializing the SDK client instance. The selected scheme will be used by default to authenticate with the API for all operations that support it. For example:
import { CloudinaryAnalysis } from "@cloudinary/analysis";
const cloudinaryAnalysis = new CloudinaryAnalysis({
security: {
cloudinaryAuth: {
apiKey: "CLOUDINARY_API_KEY",
apiSecret: "CLOUDINARY_API_SECRET",
},
},
});
async function run() {
const result = await cloudinaryAnalysis.analyze.aiVisionGeneral({
source: {
uri: "https://res.cloudinary.com/demo/image/upload/sample.jpg",
},
prompts: [
"Describe this image in detail",
"Does this image contain an insect?",
],
});
console.log(result);
}
run();
Available methods
- aiVisionGeneral - Analyze - AI Vision General
- aiVisionModeration - Analyze - AI Vision Moderation
- aiVisionTagging - Analyze - AI Vision Tagging
- captioning - Analyze - Captioning
- cldFashion - Analyze - Cld-Fashion
- cldText - Analyze - Cld-Text
- coco - Analyze - Coco
- googleLogoDetection - Analyze - Google Logo Detection
- googleTagging - Analyze - Google Tagging
- humanAnatomy - Analyze - Human Anatomy
- imageQuality - Analyze - Image Quality Analysis
- lvis - Analyze - Lvis
- shopClassifier - Analyze - Shop Classifier
- unidet - Analyze - Unidet
- watermarkDetection - Analyze - Watermark Detection
- getStatus - Get analysis task status
All the methods listed above are available as standalone functions. These functions are ideal for use in applications running in the browser, serverless runtimes or other environments where application bundle size is a primary concern. When using a bundler to build your application, all unused functionality will be either excluded from the final bundle or tree-shaken away.
To read more about standalone functions, check FUNCTIONS.md.
Available standalone functions
analyzeAiVisionGeneral
- Analyze - AI Vision GeneralanalyzeAiVisionModeration
- Analyze - AI Vision ModerationanalyzeAiVisionTagging
- Analyze - AI Vision TagginganalyzeCaptioning
- Analyze - CaptioninganalyzeCldFashion
- Analyze - Cld-FashionanalyzeCldText
- Analyze - Cld-TextanalyzeCoco
- Analyze - CocoanalyzeGoogleLogoDetection
- Analyze - Google Logo DetectionanalyzeGoogleTagging
- Analyze - Google TagginganalyzeHumanAnatomy
- Analyze - Human AnatomyanalyzeImageQuality
- Analyze - Image Quality AnalysisanalyzeLvis
- Analyze - LvisanalyzeShopClassifier
- Analyze - Shop ClassifieranalyzeUnidet
- Analyze - UnidetanalyzeWatermarkDetection
- Analyze - Watermark DetectiontasksGetStatus
- Get analysis task status
Some of the endpoints in this SDK support retries. If you use the SDK without any configuration, it will fall back to the default retry strategy provided by the API. However, the default retry strategy can be overridden on a per-operation basis, or across the entire SDK.
To change the default retry strategy for a single API call, simply provide a retryConfig object to the call:
import { CloudinaryAnalysis } from "@cloudinary/analysis";
const cloudinaryAnalysis = new CloudinaryAnalysis({
security: {
cloudinaryAuth: {
apiKey: "CLOUDINARY_API_KEY",
apiSecret: "CLOUDINARY_API_SECRET",
},
},
});
async function run() {
const result = await cloudinaryAnalysis.analyze.aiVisionGeneral({
source: {
uri: "https://res.cloudinary.com/demo/image/upload/sample.jpg",
},
prompts: [
"Describe this image in detail",
"Does this image contain an insect?",
],
}, {
retries: {
strategy: "backoff",
backoff: {
initialInterval: 1,
maxInterval: 50,
exponent: 1.1,
maxElapsedTime: 100,
},
retryConnectionErrors: false,
},
});
console.log(result);
}
run();
If you'd like to override the default retry strategy for all operations that support retries, you can provide a retryConfig at SDK initialization:
import { CloudinaryAnalysis } from "@cloudinary/analysis";
const cloudinaryAnalysis = new CloudinaryAnalysis({
retryConfig: {
strategy: "backoff",
backoff: {
initialInterval: 1,
maxInterval: 50,
exponent: 1.1,
maxElapsedTime: 100,
},
retryConnectionErrors: false,
},
security: {
cloudinaryAuth: {
apiKey: "CLOUDINARY_API_KEY",
apiSecret: "CLOUDINARY_API_SECRET",
},
},
});
async function run() {
const result = await cloudinaryAnalysis.analyze.aiVisionGeneral({
source: {
uri: "https://res.cloudinary.com/demo/image/upload/sample.jpg",
},
prompts: [
"Describe this image in detail",
"Does this image contain an insect?",
],
});
console.log(result);
}
run();
CloudinaryAnalysisError
is the base class for all HTTP error responses. It has the following properties:
Property | Type | Description |
---|---|---|
error.message |
string |
Error message |
error.statusCode |
number |
HTTP response status code eg 404 |
error.headers |
Headers |
HTTP response headers |
error.body |
string |
HTTP body. Can be empty string if no body is returned. |
error.rawResponse |
Response |
Raw HTTP response |
error.data$ |
Optional. Some errors may contain structured data. See Error Classes. |
import { CloudinaryAnalysis } from "@cloudinary/analysis";
import * as errors from "@cloudinary/analysis/models/errors";
const cloudinaryAnalysis = new CloudinaryAnalysis({
security: {
cloudinaryAuth: {
apiKey: "CLOUDINARY_API_KEY",
apiSecret: "CLOUDINARY_API_SECRET",
},
},
});
async function run() {
try {
const result = await cloudinaryAnalysis.analyze.aiVisionGeneral({
source: {
uri: "https://res.cloudinary.com/demo/image/upload/sample.jpg",
},
prompts: [
"Describe this image in detail",
"Does this image contain an insect?",
],
});
console.log(result);
} catch (error) {
// The base class for HTTP error responses
if (error instanceof errors.CloudinaryAnalysisError) {
console.log(error.message);
console.log(error.statusCode);
console.log(error.body);
console.log(error.headers);
// Depending on the method different errors may be thrown
if (error instanceof errors.ErrorResponse) {
console.log(error.data$.error); // components.ErrorObject
}
}
}
}
run();
Primary errors:
CloudinaryAnalysisError
: The base class for HTTP error responses.ErrorResponse
: Bad request.RateLimitedResponse
: Rate limited. Status code429
.
Less common errors (6)
Network errors:
ConnectionError
: HTTP client was unable to make a request to a server.RequestTimeoutError
: HTTP request timed out due to an AbortSignal signal.RequestAbortedError
: HTTP request was aborted by the client.InvalidRequestError
: Any input used to create a request is invalid.UnexpectedClientError
: Unrecognised or unexpected error.
Inherit from CloudinaryAnalysisError
:
ResponseValidationError
: Type mismatch between the data returned from the server and the structure expected by the SDK. Seeerror.rawValue
for the raw value anderror.pretty()
for a nicely formatted multi-line string.
The TypeScript SDK makes API calls using an HTTPClient
that wraps the native
Fetch API. This
client is a thin wrapper around fetch
and provides the ability to attach hooks
around the request lifecycle that can be used to modify the request or handle
errors and response.
The HTTPClient
constructor takes an optional fetcher
argument that can be
used to integrate a third-party HTTP client or when writing tests to mock out
the HTTP client and feed in fixtures.
The following example shows how to use the "beforeRequest"
hook to to add a
custom header and a timeout to requests and how to use the "requestError"
hook
to log errors:
import { CloudinaryAnalysis } from "@cloudinary/analysis";
import { HTTPClient } from "@cloudinary/analysis/lib/http";
const httpClient = new HTTPClient({
// fetcher takes a function that has the same signature as native `fetch`.
fetcher: (request) => {
return fetch(request);
}
});
httpClient.addHook("beforeRequest", (request) => {
const nextRequest = new Request(request, {
signal: request.signal || AbortSignal.timeout(5000)
});
nextRequest.headers.set("x-custom-header", "custom value");
return nextRequest;
});
httpClient.addHook("requestError", (error, request) => {
console.group("Request Error");
console.log("Reason:", `${error}`);
console.log("Endpoint:", `${request.method} ${request.url}`);
console.groupEnd();
});
const sdk = new CloudinaryAnalysis({ httpClient });
You can setup your SDK to emit debug logs for SDK requests and responses.
You can pass a logger that matches console
's interface as an SDK option.
Warning
Beware that debug logging will reveal secrets, like API tokens in headers, in log messages printed to a console or files. It's recommended to use this feature only during local development and not in production.
import { CloudinaryAnalysis } from "@cloudinary/analysis";
const sdk = new CloudinaryAnalysis({ debugLogger: console });
You can also enable a default debug logger by setting an environment variable CLOUDINARY_DEBUG
to true.
This SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning usage to a specific package version. This way, you can install the same version each time without breaking changes unless you are intentionally looking for the latest version.
While we value open-source contributions to this SDK, this library is generated programmatically. Any manual changes added to internal files will be overwritten on the next generation. We look forward to hearing your feedback. Feel free to open a PR or an issue with a proof of concept and we'll do our best to include it in a future release.