{
  "mcpServers": {
    "laddro": {
      "command": "npx",
      "args": ["-y", "@laddro/career-mcp"],
      "env": {
        "LADDRO_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

"Parse my resume from resume.pdf, tailor it for this job posting
at https://example.com/senior-engineer, and generate a cover letter.
Export both as PDFs using the Graphite template."

import requests

API_KEY = "YOUR_API_KEY"
BASE = "https://api.laddro.com/v1"
HEADERS = {"x-api-key": API_KEY, "Content-Type": "application/json"}

def tailor_for_jobs(resume_id, job_descriptions):
    results = []
    for jd in job_descriptions:
        # Tailor resume (3 credits)
        resp = requests.post(f"{BASE}/tailor", headers=HEADERS, json={
            "resumeId": resume_id,
            "jobDescription": jd
        })
        tailored = resp.json()

        # Generate cover letter (2 credits)
        resp = requests.post(f"{BASE}/cover-letters/generate", headers=HEADERS, json={
            "resumeId": resume_id,
            "jobDescription": jd
        })
        cover_letter = resp.json()

        # Export PDF (1 credit)
        resp = requests.post(f"{BASE}/export", headers=HEADERS, json={
            "resumeId": resume_id,
            "templateId": "graphite"
        })

        results.append({
            "job": jd[:50],
            "tailored": tailored,
            "coverLetter": cover_letter
        })

    return results

const response = await fetch('https://api.laddro.com/v1/tailor', {
  method: 'POST',
  headers: {
    'x-api-key': API_KEY,
    'Content-Type': 'application/json',
    'Accept': 'text/event-stream'  // Enable streaming
  },
  body: JSON.stringify({ resumeId, jobDescription })
})

const reader = response.body.getReader()
const decoder = new TextDecoder()

while (true) {
  const { done, value } = await reader.read()
  if (done) break

  const lines = decoder.decode(value).split('\n')
  for (const line of lines) {
    if (line.startsWith('data: ')) {
      const event = JSON.parse(line.slice(6))
      if (event.type === 'done') return
      // Process each section as it streams in
      agent.process(event.section, event.content)
    }
  }
}