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Let AI agents get structured judgments from real humans.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "GetABrain" yet — see the docs or source repo.
Send these two options to a real human reviewer and return schema-valid JSON: which is better for onboarding new users, plus a 1-5 rating and a short reason. Option A: ... Option B: ...
A structured JSON response with the choice, rating, and brief rationale.
Send this image/video to a real human reviewer, judge whether it matches brand guidelines, and return yes/no, issues, and suggestions.
A programmatically readable review result suitable for automation.
Have a real human complete a photo or voice feedback task: confirm whether the product appearance is correct and return structured results.
Human-collected feedback data in a format your system can process directly.
When an AI workflow needs more reliable judgment, it can call a real human for a structured JSON answer. This fits subjective review, content verification, and binary decisions.
When a team needs ratings, rankings, sentiment judgments, or A/B comparisons for text, images, audio, or video, this tool provides quality-scored human answers. The output can feed testing or analysis pipelines directly.
Useful for product teams and developers who want to programmatically recruit real humans, launch tasks, and receive results. It includes a no-card trial to validate the workflow first.
It is an MCP tool that lets an AI agent ask real humans a question and receive structured, schema-validated JSON. It emphasizes quality-scored human responses and programmatic access.
The description says it supports 16 response types, including yes/no, ratings, rankings, A/B tests, sentiment, image/video/audio review, and voice/video/photo capture.
You can sign up programmatically; the description mentions a $5 free trial credit and no credit card required. More specific setup details are not provided.
Run multi-agent debates and produce consensus recommendations with ranked options.
Run structured multi-agent debates with real-time exchange and human moderation.
Analyze AI agent traces to diagnose failures and recommend actionable improvements.
Add a local human approval gate that blocks AI agent actions.
Create and edit feedback surveys and read collected responses through AI.
Score agent outputs with guardrails, policy checks, injection, and PII detection.