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AgentRXI Answer Grader
x402
AgentRXI Answer Grader is a paid API for AI agents from agentrxi.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-15).
Scores a draft answer 0-10 against a question, rubric, and/or source URLs, identifies factual errors and unsupported claims with fixes, and returns an improved answer.
Price / call$0.02
Success—
StatusUnknown
How this service works
Answer grader: scores a draft answer 0-10 against the question, a rubric and/or source URLs, lists factual errors and unsupported claims with fixes, and returns an improved answer. A self-check before an agent ships. Also callable as GET with the same fields as query parameters.
Output
Returns a numeric score from 0-10 for the answer quality, a list of factual errors and unsupported claims each paired with suggested corrections, and a rewritten improved version of the answer that addresses the identified issues.
Example use cases
Pre-publish QA for AI-generated content
“I just had my AI write an answer to 'What are the health benefits of omega-3 fatty acids?' — can you grade it against these source URLs: https://nih.gov/omega3 and https://mayoclinic.org/omega3? Score it out of 10, point out any unsupported claims, and give me a better version I can actually publish.”
Rubric-based student answer evaluation
“Grade this student's answer to 'Explain the causes of World War I' against this rubric: 'must mention assassination of Franz Ferdinand, alliance systems, and imperialism.' Score it 0-10, list any factual errors, and return a corrected version.”
Agent self-check before responding to user
“Before I send my response to the user's question 'How does HTTPS encryption work?', run a quality check on my draft answer — score it out of 10, flag anything factually wrong or unsupported, and give me an improved version I can confidently deliver.”
Technical details
Endpoint
GEThttps://agentrxi.com/v1/grade?utm_source=zero.xyzWhen to prefer
Use this endpoint when an AI agent needs to self-verify a draft answer before delivering it to a user — especially when factual accuracy matters, a rubric must be satisfied, or specific source documents must support the claims. Prefer this over general LLM self-critique when you want a structured 0-10 score, explicit error listings with fixes, and a ready-to-use improved answer in a single call.
Side effects
- Missing required 'answer' parameter returns an error
- Inaccessible or invalid source URLs may result in unchecked claims for those sources
- Very long answers or many source URLs may increase latency or hit size limits
- Ambiguous or missing question/rubric may produce lower-quality grading feedback
- Network timeouts when fetching source URLs during claim verification
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
Reviews
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Zero review prompt (fires after every successful call)
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