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

Your assistant parses each CV, extracts the job criteria, scores every candidate 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. 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. Get weighted, explained matching criteria, with mandatory requirements flagged.
• Match candidates to jobs. Score a resume against a job from 0 to 1, with an explanation for each criterion.
• Rank candidates. Order up to 10 candidates for the same job in one call, with a score and a rationale for each.
• Normalize skills. Map free-text skills in French or English to a skills taxonomy with stable IDs.

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

All tools only read and analyse data. They never change anything in your systems.

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

How screening works

flowchart LR
    J[Job description] --> C[extract_job_criteria]
    R[Resume files] --> P[parse_resume]
    C --> M[match_candidate]
    P --> M
    P --> K[rank_candidates]
    J --> K
    M --> S[Shortlist with explanations]
    K --> S

{
  "score": 0.89,
  "summary": "Profil très aligné : React, TypeScript et l’expérience demandée sont démontrés. Le niveau d’anglais reste à confirmer.",
  "evaluated_criteria": [
    {
      "id": "crit_1",
      "label": "Maîtrise de React",
      "weight": 3,
      "is_mandatory": true,
      "match_status": "ideal",
      "match_explanation": "Le CV décrit une équipe React dirigée depuis 2022 sur une plateforme en production."
    }
  ]
}

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 must-haves."
• "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 and explain why the top 3 stand out."
• "Does this candidate meet every mandatory criterion? If not, which ones are missing?"

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

Each successful tool call costs 1 HireLayer credit. A rank_candidates call costs 1 credit whatever the number of candidates. Failed calls are not charged.

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. By default HireLayer stores the original file and returns a link to it in info_resume.url. Set do_not_store_data: true in a call so the file is not stored.
• 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, matching, ranking 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 labels and rationales, match summaries and explanations, ranking rationales) is in French; your assistant translates it when it answers in another language. Skills resolution returns French or English labels.

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