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Citiview Answer Grader
x402
Citiview Answer Grader is a paid API for AI agents from agents.citiviewgroup.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-14).
Scores a draft answer 0-10 against a question, rubric, and/or source URLs, identifies factual errors and unsupported claims, 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 to 10 rating answer quality, a list of factual errors and unsupported claims with suggested fixes, and an improved version of the original answer that addresses the identified issues.
Example use cases
Pre-flight check for agent responses
“Before my AI assistant sends this response to the user, score it against the question 'What are the health benefits of omega-3 fatty acids?' and check for unsupported claims using these sources: https://nih.gov/omega3 | https://mayoclinic.org/omega3. Flag any errors and give me an improved version.”
Rubric-based student answer grading
“Grade this student answer against the rubric 'Must mention supply, demand, and equilibrium price; answer must be 2-3 sentences': The answer is 'Prices go up when people want more stuff.' Score it out of 10, list what's missing, and suggest a better answer.”
Fact-checking a research summary
“Check this summary I wrote about climate change: 'Global temperatures have risen by 5 degrees Celsius since 1900 due to deforestation alone.' Score it against the question 'What are the main drivers of climate change?' using these sources: https://ipcc.ch/report | https://noaa.gov/climate, and tell me what's factually wrong.”
Technical details
Endpoint
GEThttps://agents.citiviewgroup.com/v1/grade?utm_source=zero.xyzWhen to prefer
Use this endpoint when an AI agent needs to self-check or quality-gate a generated answer before delivering it to a user, when you need rubric-based scoring of free-text responses, or when you want to verify factual accuracy against specific source documents. Prefer this over generic LLM self-critique when you need a structured 0-10 score, explicit error listings, and a ready-to-use improved answer in one call.
Side effects
- Missing required 'answer' parameter returns an error
- Invalid or unreachable source URLs may result in unchecked claims
- Very long answers or many sources may reduce grading accuracy
- No question or rubric provided means grading is based on answer coherence alone
- Rate limiting or payment failure returns a 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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