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clouatre.ca Resume Evaluator
x402
clouatre.ca Resume Evaluator is a paid API for AI agents from clouatre.ca, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-31).
Scores a resume against a job description across multiple dimensions, returning weighted scores, a pass/fail verdict, and flagged red flags.
Price / call$0.005
Success—
StatusUnknown
How this service works
AI and platform engineering insights from a Field CTO and AWS veteran. Practical guides for CTOs and engineering leaders.
Output
Returns a JSON object with a total score, per-dimension scores (e.g. ats_parseable, contact_complete, executive_framing, impact_quantified), a findings array with per-check pass/fail and details, and a warnings array for any processing issues such as upstream timeouts.
Example use cases
Job application fit check
“I'm about to apply for this senior product manager role — can you score my resume against the job description and tell me where I'm weak, what red flags might get me screened out, and how ATS-parseable it looks? Here's my resume and the full job posting text.”
Resume quality audit before submitting
“Before I send in my application, run my resume through an evaluation against this job description and give me a breakdown of dimension scores — especially whether my impact statements are quantified and if my contact info is complete. Output the results as JSON.”
Recruiting pipeline pre-screen
“I need to quickly screen a candidate's resume against our open engineering lead job description — score it on ATS compatibility, executive framing, and impact quantification, and flag any warnings or red flags so I can decide if it's worth a human review.”
Technical details
Endpoint
POSThttps://clouatre.ca/v1/resume-eval?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a structured, multi-dimensional machine-readable score of resume-to-job-description fit, especially when ATS compatibility, contact completeness, impact quantification, and executive framing are the evaluation axes needed. Prefer it over generic LLM prompting when you want a deterministic, reproducible scoring output with explicit findings and red-flag detection rather than a narrative critique.
Side effects
- Resume or job description text exceeds character limits (30000 and 6000 respectively) causing validation error
- Invalid format enum value returns a schema validation error
- Upstream timeout during scoring returns a WARNING with code UPSTREAM_TIMEOUT in the warnings array
- Missing required fields (resume_text or job_description) returns a 400-level error
- Payment failure via x402 protocol blocks the request before processing
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| format | string | |
| resume_text | string | |
| job_description | string |
Output
Schema · Response
{
"type": "json",
"example": {
"total": 3,
"scores": {
"ats_parseable": 1,
"contact_complete": 1,
"executive_framing": 0,
"impact_quantified": 1
},
"findings": [
{
"pass": true,
"check": "impact_quantified",
"detail": "..."
}
],
"warnings": [
{
"code": "UPSTREAM_TIMEOUT",
"message": "..."
}
]
}
}Tags
x402
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