MachineTutors AI API

Name: MachineTutors AI API
Brand: MachineTutors AI API
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLVisionapiKey5 EndpointsREST

For Agents

Classify, detect objects in, moderate, search and auto-tag images using MachineTutors' five computer vision endpoints.

Install Jentic One Beta

Connect the MachineTutors AI API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the MachineTutors AI API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with MachineTutors AI API.

Classify the dominant subject of an image and return label confidence scores via /classify

Detect bounding boxes and labels for objects in a scene through /detect

Flag adult, violent or otherwise unsafe content with the /moderate endpoint

GET STARTED

Start building with MachineTutors AI API

Explore with Jentic

View OpenAPI Document

Use Cases

Patterns agents use MachineTutors AI API for, with concrete tasks.

★ E-Commerce Product Auto-Tagging

Eliminate manual data entry on new product uploads by sending the listing image to /tag and receiving a list of descriptive tags. The endpoint analyses the photo and returns labels suitable for filters, facets and search indexing. Useful for catalogue managers loading hundreds of SKUs where consistent tagging would otherwise require expensive manual labelling.

Send a product photo to POST /tag and persist the returned tags as facet values on the listing record

Content Moderation for User Uploads

Screen images uploaded to a community platform for unsafe content before they reach other users. POST /moderate returns category-level scores so moderation logic can auto-block, queue for review, or publish based on policy. Suitable for marketplaces, social apps and dating sites that cannot rely solely on report-based moderation.

POST the uploaded image to /moderate and route it to the human review queue if any unsafe category score exceeds 0.7

Visual Similarity Search for Catalogues

Power 'find similar items' UX inside an e-commerce or stock-imagery app. POST /search accepts an image and returns the most visually similar items already indexed in the catalogue. The agent can then surface those results as recommendations, dupe candidates or replacement suggestions when a SKU goes out of stock.

Submit a customer photo to POST /search and return the top 5 visually similar SKUs from the indexed catalogue

AI Agent Vision Workflow

Plug computer vision into a wider AI agent flow without managing the MachineTutors API key. Through Jentic the agent searches for the vision operation it needs, loads the input schema for /classify, /detect, /moderate, /search or /tag, and executes with credentials brokered by the vault. Useful for multi-step pipelines where a vision call is one step in a longer chain.

Search Jentic for 'classify objects in an image', load /classify, and execute against a user-supplied image URL

Key Endpoints

5 endpoints — jentic publishes the only available openapi specification for machinetutors ai api, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/classify

Classify objects in an image

POST

/detect

Detect objects with bounding boxes

POST

/moderate

Score image content for safety violations

POST

/search

Visual similarity search

POST

/tag

Auto-tag product imagery

POST

/classify

Classify objects in an image

POST

/detect

Detect objects with bounding boxes

POST

/moderate

Score image content for safety violations

POST

/search

Visual similarity search

POST

/tag

Auto-tag product imagery

Why Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

MachineTutors API keys are stored encrypted in the Jentic vault (MAXsystem). Agents receive scoped access tokens — the raw key never enters the agent's context, so prompt leakage cannot expose the underlying credential.

Intent-based discovery

Agents search by intent (e.g. 'classify an image' or 'auto tag a product photo') and Jentic returns the matching MachineTutors operation with its input schema, so the agent picks /classify, /detect, /moderate, /search or /tag without consulting docs.

Time to first call

Direct MachineTutors integration: half a day for auth, multipart upload handling and error mapping. Through Jentic: under 30 minutes — search, load schema, execute.

FAQs

Specific to using MachineTutors AI API through Jentic.

Why is there no official OpenAPI spec for MachineTutors AI API?

MachineTutors does not publish an OpenAPI specification on its developer site. Jentic generates and maintains this spec so that AI agents and developers can call the MachineTutors vision endpoints via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the MachineTutors AI API use?

The API uses an API key authenticated via the ApiKeyAuth scheme defined in the spec, passed as a request header. Through Jentic the key is stored encrypted in the MAXsystem vault, so an agent calls /classify or /moderate with a scoped token instead of handling the raw key.

Can I detect multiple objects per image with MachineTutors?

Yes. POST /detect returns an array of detections with bounding boxes and confidence scores, so a single call can surface every object the model identifies in the supplied image. /classify by contrast returns a single dominant label per image.

What are the rate limits for the MachineTutors AI API?

Per-endpoint rate limits are not defined in the spec; MachineTutors typically enforces them at the account tier set when the API key is issued. Check your account dashboard at machinetutors.com for the limits attached to your key.

How do I moderate images with MachineTutors through Jentic?

Run pip install jentic, search for 'moderate an image', and Jentic surfaces POST /moderate with its input schema. Execute with the image URL or upload, then inspect the returned category scores against your moderation policy thresholds.

Does MachineTutors support video moderation?

The published spec defines only image-based endpoints (/classify, /detect, /moderate, /search, /tag). For video moderation, sample frames from the video and submit each frame to /moderate, or choose a provider such as Imagga that handles video natively.

MachineTutors AI API

Connect the MachineTutors AI API to your agent

Capabilities

Start building with MachineTutors AI API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs