Amazon Mechanical Turk

Name: Amazon Mechanical Turk API
Brand: Amazon Mechanical Turk
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferencehmac39 EndpointsREST

For Agents

Publish, manage and review human-completed tasks (HITs) on Amazon Mechanical Turk — create HITs, approve or reject assignments, manage worker qualifications, and pay bonuses.

Connect the Amazon Mechanical Turk to your agent

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

Install Jentic One Beta (Self-Hosted)

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

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 Amazon Mechanical Turk API.

Publish Human Intelligence Tasks (HITs) with title, description, reward, lifetime, and assignment count

Create reusable HITTypes with shared properties for batches of similar tasks

List and review submitted Assignments and approve, reject, or send back to Workers

Pay performance bonuses to Workers and notify them of incentives

GET STARTED

Start building with Amazon Mechanical Turk API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for Amazon Mechanical Turk, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Amazon Mechanical Turk, keeping it validated and agent-ready. Mechanical Turk (MTurk) is AWS's human-in-the-loop crowdsourcing platform — Requesters publish Human Intelligence Tasks (HITs), Workers complete them, and Requesters review the resulting Assignments. The 39 endpoints cover the full Requester lifecycle: CreateHIT and CreateHITWithHITType, ListHITs, accept/reject Assignments, send bonuses, manage Qualifications and QualificationRequests, and notify or block Workers.

Use Cases

Patterns agents use Amazon Mechanical Turk API for, with concrete tasks.

★ Crowdsource Labels for Machine Learning Training Data

ML teams need labelled image, text or audio data to train supervised models. Mechanical Turk lets a Requester publish HITs with custom HTML/QuestionForm content, set a per-assignment reward and lifetime, and collect responses from a global Worker pool. The CreateHIT and ListAssignmentsForHIT endpoints, combined with QualificationRequirements, support producing high-quality labelled datasets at low unit cost.

Call CreateHIT with Title='Label image content', Reward='0.10', LifetimeInSeconds=86400, MaxAssignments=3 and a QuestionForm XML, then poll ListAssignmentsForHIT and approve qualifying responses

Build a Vetted Worker Pool With Custom Qualifications

Sensitive or domain-specific tasks (medical text annotation, multilingual transcription) require Workers with verified skills. MTurk's Qualifications API lets a Requester define a custom QualificationType, grant it via QualificationRequest review or a built-in Qualification test, and then require it on subsequent HITs. The agent can review pending QualificationRequests and accept or reject them based on a custom rubric.

Call CreateQualificationType, then list pending requests with ListQualificationRequests and call AcceptQualificationRequest for those who pass a rubric check

Run a Human-in-the-Loop Validation Step

Generative AI pipelines often need a human review step on uncertain model outputs. An agent can take low-confidence outputs from a model, publish them as small HITs (each Assignment shows two model outputs and asks a Worker to pick the better one), and feed the resulting human preferences back into reward modelling. The same primitives — CreateHIT, list assignments, approve, bonus — drive both data labelling and HITL validation.

Call CreateHITWithHITType for each low-confidence output using a pre-built HITType, collect assignments, and aggregate Worker preferences for a feedback dataset

AI Agent Integration for Crowdsourced Workflows

Through Jentic, an AI agent can orchestrate the full Requester lifecycle on Mechanical Turk — publish HITs, list and review assignments, pay bonuses, manage worker pools — without writing custom boto3 logic. Jentic returns the input schema for each MTurk operation and brokers SigV4 credentials so an LLM can drive crowdsourcing workflows from natural-language instructions.

Resolve the intent 'publish a Mechanical Turk HIT' via Jentic search, load the CreateHIT schema, and execute it with title, reward, lifetime, max assignments and a QuestionForm body

Key Endpoints

39 endpoints — jentic publishes the only available openapi specification for amazon mechanical turk, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/#X-Amz-Target=MTurkRequesterServiceV20170117.CreateHIT

Publish a new Human Intelligence Task

POST

/#X-Amz-Target=MTurkRequesterServiceV20170117.ListHITs

List the Requester's HITs with paging

POST

/#X-Amz-Target=MTurkRequesterServiceV20170117.ApproveAssignment

Approve a submitted assignment and pay the Worker

POST

/#X-Amz-Target=MTurkRequesterServiceV20170117.RejectAssignment

Reject an assignment with feedback

POST

/#X-Amz-Target=MTurkRequesterServiceV20170117.SendBonus

Send a bonus payment to a Worker

POST

/#X-Amz-Target=MTurkRequesterServiceV20170117.CreateQualificationType

Create a custom qualification type

POST

/#X-Amz-Target=MTurkRequesterServiceV20170117.AcceptQualificationRequest

Approve a Worker's qualification request

POST

/#X-Amz-Target=MTurkRequesterServiceV20170117.CreateHIT

Publish a new Human Intelligence Task

POST

/#X-Amz-Target=MTurkRequesterServiceV20170117.ListHITs

List the Requester's HITs with paging

POST

/#X-Amz-Target=MTurkRequesterServiceV20170117.ApproveAssignment

Approve a submitted assignment and pay the Worker

POST

/#X-Amz-Target=MTurkRequesterServiceV20170117.RejectAssignment

Reject an assignment with feedback

POST

/#X-Amz-Target=MTurkRequesterServiceV20170117.SendBonus

Send a bonus payment to a Worker

Why though Jentic?

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

Credential isolation

AWS access keys live in the Jentic MAXsystem vault. Jentic signs each MTurk request with SigV4 on the agent's behalf, so an agent driving crowdsourcing workflows never sees raw IAM credentials.

Intent-based discovery

Agents search by intent (e.g., 'publish a Mechanical Turk HIT') and Jentic returns the matching MTurk operation with its input schema, so the agent can call CreateHIT, ApproveAssignment or SendBonus without consulting the AWS docs.

Time to first call

Direct MTurk Requester integration: 2-3 days for SigV4 signing, QuestionForm XML shaping and assignment polling. Through Jentic: a few hours — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Amazon Augmented AI (A2I) Runtime

A2I provides a higher-level human-review workflow that can route tasks to MTurk, internal teams, or vendors

Choose A2I when an agent wants a managed human-review workflow with policy-driven routing. Use raw MTurk when full control over HITs and Worker pools is required.

Complementary

Amazon Comprehend

Comprehend pre-classifies text so an agent can send only low-confidence cases to MTurk Workers

Use upstream of MTurk to filter the inputs that need human review, reducing the cost of human-in-the-loop workflows.

Complementary

Amazon SageMaker

SageMaker trains models on the labelled data MTurk produces and serves them for inference

Use to close the loop — collect labels from MTurk, train a SageMaker model, and serve predictions back to the application.

FAQs

Specific to using Amazon Mechanical Turk API through Jentic.

Why is there no official OpenAPI spec for Amazon Mechanical Turk?

AWS does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Amazon Mechanical Turk 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 Mechanical Turk Requester API use?

The MTurk Requester API is signed with AWS Signature Version 4 using IAM access keys. Through Jentic, those AWS credentials live in the MAXsystem vault and are used to sign each MTurk request on the agent's behalf — the raw access key never reaches the agent.

Can I create a HIT with custom HTML or external page content?

Yes. CreateHIT accepts either a Question (XML — QuestionForm or HTMLQuestion) describing the task content directly, or an HITLayoutId pointing at a published layout. HTMLQuestion can iframe an externally hosted page for richer interfaces such as bounding-box image annotation.

What are the rate limits for Mechanical Turk?

MTurk enforces per-Requester throttling on the control plane; specific TPS values are not published in the spec. Treat throttling as expected, especially for ListAssignmentsForHIT during bulk reviews, and apply exponential backoff between calls.

How do I approve a batch of assignments through Jentic?

Use the Jentic search query 'list MTurk assignments', load ListAssignmentsForHIT, then for each Submitted assignment call ApproveAssignment with the AssignmentId and an optional RequesterFeedback string. Jentic signs each request with SigV4 against your stored AWS credentials.

Is Mechanical Turk free?

MTurk charges a fee on top of each Worker reward and bonus payment, with higher fees for HITs that require custom Qualifications. There is no charge for control-plane API calls themselves, but a Requester account must be funded to publish paid HITs.