SiteCheck Resume Parser

x402

SiteCheck Resume Parser is a paid API for AI agents from api.sitecheck-api.workers.dev, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-20).

Parses a CV/resume from a PDF URL, base64-encoded PDF, or plain text and extracts structured candidate information including name, email, skills, experience, education, and certifications.

Price / call$0.005

Seller volume →—

Success—

StatusUnknown

How this service works

Structured data from a CV or resume: name, contact details, location, links, summary, skills, languages, certifications, work experience and education, with null or empty lists where the text has nothing. Plain text, or a PDF with a text layer by URL or base64. Dates are kept as written. Not live-checked: Workers AI is checked by the owner after the merge.

Output

Returns a JSON object containing the parsed resume fields: candidate name, email, phone, location, links, a list of skills, a summary, education history, work experience (with start/end dates, title, company, and description), certifications, and detected languages. Also includes the model used, the source type (URL, text, or base64), the timestamp, and a disclaimer noting results should be verified against the original document.

Example use cases

Screening applicants for an open role

“I just received a batch of resumes as PDFs. Can you parse this one at https://example.com/applicant-resume.pdf and pull out the candidate's name, email, skills, and years of experience so I can quickly screen them?”

Building a candidate database from uploaded CVs

“I have a CV uploaded as a base64 PDF — can you extract all the structured info from it, including work history, education, certifications, and contact details, so I can store it in our ATS?”

Extracting skills from a pasted CV text

“Here's the raw text from a candidate's CV — can you parse it and give me a structured breakdown of their skills, job titles, companies they've worked at, and any languages or certifications they listed?”

Technical details

Endpoint

POSThttps://api.sitecheck-api.workers.dev/api/resume/parse?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need to extract structured candidate data (name, email, skills, experience, education) from a resume in PDF or text form without building your own parsing pipeline. It accepts PDFs by URL, base64 upload, or raw text, making it versatile for ATS integrations, recruiting workflows, and candidate screening automation. Prefer it over generic OCR or document extraction tools when the target document is specifically a CV/resume and you need HR-domain structured output.

Side effects

PDF URL is inaccessible or returns a non-200 response — parsing fails with an error
PDF has no text layer (image-only/scanned PDF) — extraction may be empty or inaccurate
Base64 payload exceeds 15 MB limit — request rejected
None of the three input fields (url, text, pdf_base64) provided — validation error
LLM extraction may miss or hallucinate fields — disclaimer included in every response
Malformed PDF causes parsing failure

Input

Schema · Request body

Field	Type	Description
url	string	https link to a PDF CV with a text layer (JSON body field)
text	string	The CV text (JSON body field); or give url or pdf_base64 instead
pdf_base64	string	The PDF as base64, up to 15 MB (JSON body field)

Output

Schema · Response

{
  "type": "json",
  "example": {
    "model": "@cf/meta/llama-3.3-70b-instruct-fp8-fast",
    "resume": {
      "name": "Jane Doe",
      "email": "jane@example.com",
      "links": [],
      "phone": null,
      "skills": [
        "Python",
        "SQL"
      ],
      "summary": null,
      "location": null,
      "education": [],
      "languages": [
        "English"
      ],
      "experience": [
        {
          "end": "present",
          "start": "2019",
          "title": "Software engineer",
          "company": "Acme Ltd",
          "description": null
        }
      ],
      "certifications": []
    },
    "source": "text in the request",
    "checkedAt": "2026-10-15T00:00:00.000Z",
    "disclaimer": "Model extraction from the CV text. Check the details against the CV."
  }
}

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