Resume Parser API

The XeCubes AI Resume Parser extracts structured candidate entities from raw resumes (PDF, DOCX, TXT) and computes a multi-dimensional job suitability score against your job requirements.

---

1. Upload Resume for Analysis

Submit a multipart form-data payload containing the candidate's resume and target job_id.

POSThttps://api.xecubes.com/recruiter/jobs/vendor/resume-analysis

Form Parameters

Field	Type	Required	Description
resume	File	Yes	PDF or DOCX resume document (Max size: 15MB).
job_id	String	Yes	Target Job UUID to score candidate against.
callback_url	String	No	HTTPS webhook URL to receive parser results when completed.

cURL

curl -X POST "https://api.xecubes.com/recruiter/jobs/vendor/resume-analysis" \
  -H "X-API-Key: xh_live_your_secret_key" \
  -F "resume=@/path/to/candidate_resume.pdf" \
  -F "job_id=job_uuid_12345678" \
  -F "callback_url=https://yourserver.com/webhooks/resume-parsed"

Node.js

const formData = new FormData();
formData.append('resume', fs.createReadStream('./candidate_resume.pdf'));
formData.append('job_id', 'job_uuid_12345678');
formData.append('callback_url', 'https://yourserver.com/webhooks/resume-parsed');

const response = await fetch('https://api.xecubes.com/recruiter/jobs/vendor/resume-analysis', {
  method: 'POST',
  headers: {
    'X-API-Key': 'xh_live_your_secret_key'
  },
  body: formData
});
const result = await response.json();
console.log('Queued Analysis:', result);

Python

import requests

files = {'resume': open('candidate_resume.pdf', 'rb')}
data = {
    'job_id': 'job_uuid_12345678',
    'callback_url': 'https://yourserver.com/webhooks/resume-parsed'
}
headers = {'X-API-Key': 'xh_live_your_secret_key'}

response = requests.post(
    "https://api.xecubes.com/recruiter/jobs/vendor/resume-analysis",
    headers=headers,
    files=files,
    data=data
)
print(response.json())

Go

package main

import (
	"bytes"
	"io"
	"mime/multipart"
	"net/http"
	"os"
)

func main() {
	file, _ := os.Open("candidate_resume.pdf")
	defer file.Close()

	body := &bytes.Buffer{}
	writer := multipart.NewWriter(body)
	part, _ := writer.CreateFormFile("resume", "candidate_resume.pdf")
	io.Copy(part, file)
	_ = writer.WriteField("job_id", "job_uuid_12345678")
	_ = writer.WriteField("callback_url", "https://yourserver.com/webhooks/resume-parsed")
	writer.Close()

	req, _ := http.NewRequest("POST", "https://api.xecubes.com/recruiter/jobs/vendor/resume-analysis", body)
	req.Header.Set("X-API-Key", "xh_live_your_secret_key")
	req.Header.Set("Content-Type", writer.FormDataContentType())

	client := &http.Client{}
	resp, _ := client.Do(req)
	defer resp.Body.Close()
}

Immediate Queue Response

{
  "status": "success",
  "message": "resume analysis created and queued",
  "data": {
    "analysis_id": "anal_987654321"
  }
}

---

2. Retrieve Parser Status & Results

Query the processing status and candidate extraction results using the analysis_id returned during upload.

GEThttps://api.xecubes.com/recruiter/jobs/vendor/resume-analysis/:analysis_id

cURL

curl -X GET "https://api.xecubes.com/recruiter/jobs/vendor/resume-analysis/anal_987654321" \
  -H "X-API-Key: xh_live_your_secret_key"

Node.js

const res = await fetch('https://api.xecubes.com/recruiter/jobs/vendor/resume-analysis/anal_987654321', {
  headers: {
    'X-API-Key': 'xh_live_your_secret_key'
  }
});
const report = await res.json();
console.log('Candidate Score:', report.data.analysis.suitability_score);

Comprehensive Parsed Output

{
  "status": "success",
  "data": {
    "id": "anal_987654321",
    "status": "completed",
    "candidate": {
      "name": "Sarah Connor",
      "email": "[email protected]",
      "phone": "+1 415 555 0192",
      "linkedin_url": "https://linkedin.com/in/sarahconnor",
      "github_url": "https://github.com/sconnor",
      "location": "San Francisco, CA"
    },
    "analysis": {
      "suitability_score": 94.5,
      "analysis_summary": "Exceptional fit for Senior AI Infrastructure role. Demonstrates 6+ years of distributed systems engineering, deep PyTorch experience, and high-throughput vector database deployments.",
      "match_breakdown": {
        "technical_skills": 96.0,
        "experience_depth": 92.0,
        "education_pedigree": 95.0
      },
      "key_skills": [
        "Python",
        "TypeScript",
        "PgVector",
        "PyTorch",
        "Kubernetes",
        "Distributed Systems"
      ],
      "experience_years": 6.8,
      "seniority_level": "Senior Staff"
    }
  }
}