HireLayer MCP Server

Resume parsing, resume scoring and talent pool search for AI agents.

The official Model Context Protocol server for HireLayer. Parse resumes and CVs, turn job descriptions into criteria, score and rank resumes against a job, and search the candidates and jobs you already have, from Claude, ChatGPT, Cursor, VS Code, Codex or any MCP client.

Server URL: https://hirelayer.co/mcp · sign in with your HireLayer account, no API key to copy.

"Screen these 3 resumes against the Senior React job and tell me who to interview."

Your assistant parses each CV, scores every candidate against the job criterion by criterion and explains the shortlist.

Contents

• What you can do
• Quick start
• Install in your MCP client
• Tools
• Example prompts
• Pricing and credits
• Data and privacy
• Troubleshooting
• FAQ

What you can do

• Parse resumes and CVs (Resume Parser). Turn PDF, Word, image and other files into structured JSON: contact details, work experience, education, languages, skills and the full text. Scanned resumes go through OCR, and the resume language is detected.
• Turn a job description into criteria (Job Criteria Extractor). Get the typed, weighted criteria the posting states, each marked required, preferred or bonus, in English, French, German, Spanish or Portuguese. Free.
• Score and rank resumes (Resume Scorer). Score up to 50 resumes against a job in one call, from 0 to 1, with a verdict, a summary and the evidence for each criterion, best first. Ask for a precise ranking to re-rank the 10 best with a reason each.
• Search your talent pool (Talent Pool Search). Find the resumes already in your HireLayer database closest to a job, around a city if you like.
• Recommend jobs (Job Recommendations). Find your open jobs closest to a candidate.
• Normalize skills (Skills Normalizer). Map free-text skills to a skills taxonomy with stable IDs, named in French or English.

Use it to screen applicants in a chat, build a recruiting agent, enrich an ATS, or prototype HR tech features without writing integration code.

Quick start

1. Add the server URL https://hirelayer.co/mcp in your AI app, or click a one-click install button above.
2. Sign in to HireLayer when your app asks, and allow access. No account yet? Sign up for free: 50 credits a month, no card required.
3. Ask your assistant. For example: attach a resume and ask "Summarize this candidate."

To try it without your own data, use the sample job and resumes in examples/. The repository ships a .mcp.json: clone it, export HIRELAYER_API_KEY, open the folder in Claude Code and it offers to enable the local server.

Install in your MCP client

Hosted server (recommended)

Connect to https://hirelayer.co/mcp and sign in to HireLayer. Nothing to install, nothing to keep up to date.

Clients that cannot sign in with OAuth can send a HireLayer API key instead: Authorization: Bearer your-api-key.

The hosted parse_resume takes a file attached in ChatGPT, a public HTTPS URL, a small file in base64 or the resume text. To parse files from your disk, run the server locally.

Local server (npx)

Runs on your machine with an API key: sign up, then copy your key from Dashboard → API keys and replace your-api-key below.

claude mcp add hirelayer --env HIRELAYER_API_KEY=your-api-key -- npx -y hirelayer-mcp

{
  "mcpServers": {
    "hirelayer": {
      "command": "npx",
      "args": ["-y", "hirelayer-mcp"],
      "env": { "HIRELAYER_API_KEY": "your-api-key" }
    }
  }
}

{
  "mcpServers": {
    "hirelayer": {
      "command": "npx",
      "args": ["-y", "hirelayer-mcp"],
      "env": { "HIRELAYER_API_KEY": "your-api-key" }
    }
  }
}

{
  "inputs": [
    { "type": "promptString", "id": "hirelayer_api_key", "description": "HireLayer API key", "password": true }
  ],
  "servers": {
    "hirelayer": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "hirelayer-mcp"],
      "env": { "HIRELAYER_API_KEY": "${input:hirelayer_api_key}" }
    }
  }
}

{
  "mcpServers": {
    "hirelayer": {
      "command": "npx",
      "args": ["-y", "hirelayer-mcp"],
      "env": { "HIRELAYER_API_KEY": "your-api-key" }
    }
  }
}

codex mcp add hirelayer --env HIRELAYER_API_KEY=your-api-key -- npx -y hirelayer-mcp

gemini mcp add -e HIRELAYER_API_KEY=your-api-key hirelayer npx -y hirelayer-mcp

docker build -t hirelayer-mcp .
docker run -i --rm -e HIRELAYER_API_KEY=your-api-key hirelayer-mcp

Tools

HireLayer keeps the resumes, jobs and scores in your HireLayer account, as the REST API does, so a resume or a job-resume pair already processed costs nothing the second time and the two searches have something to search. normalize_skills stores nothing. No tool deletes anything or changes your other systems.

Several client companies? Every tool except normalize_skills takes an optional workspace (for example acme-corp), sent as the HireLayer-Workspace header: each client's jobs, resumes and scores stay apart. A new id creates a new workspace; without one, calls use your default workspace.

Upgrading from 1.x: match_candidate and rank_candidates became score_resumes, and resolve_skills became normalize_skills. See the changelog.

The server also ships prompts that clients show as ready-made commands:

How screening works

flowchart LR
    J[Job description] --> S[score_resumes]
    R[Resume files] --> P[parse_resume]
    P -- application_id --> S
    T[Resume texts] --> S
    J -. review criteria first .-> C[extract_job_criteria]
    C -. job_id .-> S
    D[(Your talent pool)] --> Q[search_resumes]
    Q -- application_ids --> S
    S --> L[Shortlist with evidence]

{
  "job": {
    "job_id": "job_8f14e45fceea167a5a36dedd4bea2543",
    "title": "Senior React Developer",
    "language": "en"
  },
  "precise_ranking": true,
  "scores": [
    {
      "rank": 1,
      "application_id": "text_c4ca4238a0b923820dcc509a6f75849b",
      "reference": "alex-morgan",
      "evaluation_status": "completed",
      "score": 0.91,
      "verdict": "highly_relevant",
      "summary": "Strong fit: 8 years of React and TypeScript, team lead, automated testing with Jest and Playwright. Health tech experience to confirm.",
      "ranking_reason": "The only candidate who has led a React and TypeScript team in production.",
      "required_missing": [],
      "to_verify": [],
      "criteria": [
        {
          "criterion_id": "crit_2",
          "label": "React experience",
          "requirement": "required",
          "weight": 3,
          "status": "met",
          "score": 1,
          "evidence": "Lead Front-End Engineer, Medisoft: 'Lead a team of 4 developers on a React 18 + TypeScript appointment platform'",
          "explanation": "Over 3 years of React, the last ones leading a team on a production platform."
        }
      ]
    }
  ],
  "credits_used": 3
}

Example prompts

Recruiters and hiring managers

• "Parse ~/Downloads/jane-doe.pdf and summarize her experience in five bullet points."
• "Here is our job description for a Senior Data Engineer. Extract the criteria, then tell me which ones are required."
• "Score the resumes in ~/candidates/ against this job and give me a shortlist table with scores and main gaps."
• "Rank these 8 candidates for the Account Executive role with a precise ranking and explain why the top 3 stand out."
• "Does this candidate meet every required criterion? If not, which ones are missing?"
• "Which candidates already in my HireLayer database fit this job? Keep those within 50 km of Lyon."
• "Which of our open jobs suit this candidate?"
• "For our client Acme Corp (workspace acme-corp), score these resumes against their Product Manager job."

Developers and HR tech teams

• "Parse this resume and map the result to our ATS candidate schema: { name, email, current_title, skills[] }."
• "Normalize this skills section: Pack Office (Word, Excel), React.js, anglais courant, gestion de projet."
• "Write a TypeScript function that sends a resume to the HireLayer API, using the JSON this tool returned as the expected type."

Try it now with the sample files

• "Screen the resumes in examples/ against examples/job-senior-react-developer.md."

Pricing and credits

Failed calls and resumes that fail are not charged, and a call refused for lack of credits processes nothing.

Data and privacy

• The hosted server receives only what your assistant sends to HireLayer tools. Each app gets access only after you sign in and allow it, and you can disconnect it at any time.
• The local server runs on your machine and calls the HireLayer API over HTTPS with your API key. It has no telemetry.
• parse_resume reads only the file you name. HireLayer stores the resume in your account under its application_id, keeps the original file and returns a link to it in resume.info_resume.url. On the Enterprise plan, set do_not_store_data: true in a call so nothing of the parse is kept.
• The jobs, resumes and scores the tools process stay in your HireLayer account, in the workspace of the call, so you can score and search them again later.
• See the privacy policy and the security policy.

Resumes contain personal data. Use the tools in line with your hiring process and the rules that apply to you, such as GDPR. Scores support human decisions; they don't replace them.

Configuration

Troubleshooting

To debug, run the server in the MCP Inspector:

HIRELAYER_API_KEY=your-api-key npx @modelcontextprotocol/inspector npx -y hirelayer-mcp

FAQ

Is HireLayer an ATS? No. HireLayer provides the AI building blocks of recruiting software: resume parsing, scoring and ranking, talent pool search and skills. Use them on their own through MCP, or plug them into your ATS or HR tech product through the REST API.

Which language are the results in? The text that HireLayer writes (criteria, summaries, explanations and ranking reasons) is in the job's language: English, French, German, Spanish or Portuguese, and English for a job written in another language. Choose it with language (en, fr, de, es or pt). Evidence quotes the resume in its own language. Skills normalization returns French or English labels.

Which resume languages are supported? The parser detects the main language of each resume and returns it in resume.info_resume.language.

Can I use the REST API directly? Yes. See the API reference, the OpenAPI spec and llms.txt for agents.

Do I need an API key? Not with the hosted server: you sign in to HireLayer and allow access, and the app's calls use your plan's credits. Each connected app appears as an "(MCP)" key in Dashboard → API keys; revoke it to disconnect the app. The local server uses an API key.

Development

git clone https://github.com/hirelayer/hirelayer-mcp.git
cd hirelayer-mcp
npm install
npm test
HIRELAYER_API_KEY=your-api-key npx @modelcontextprotocol/inspector node dist/index.js

See CONTRIBUTING.md. Report bugs in GitHub issues and vulnerabilities as described in SECURITY.md.

Links

• Website: hirelayer.co
• API reference: hirelayer.co/api-docs
• OpenAPI: hirelayer.co/openapi.json
• Docs for agents: hirelayer.co/llms.txt
• Contact: contact@hirelayer.co

License

MIT