HireLayer

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 Code

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

Claude Desktop

Open Settings → Developer → Edit Config and add the server to claude_desktop_config.json:

{

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

Restart Claude Desktop.

Cursor

Add this to ~/.cursor/mcp.json (all projects) or .cursor/mcp.json (one project):

{

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

VS Code (GitHub Copilot)

Add this to .vscode/mcp.json. VS Code asks for your API key and stores it securely:

{

"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}" } } } }

Windsurf

Add this to ~/.codeium/windsurf/mcp_config.json:

{

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

OpenAI Codex CLI

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

Gemini CLI

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

Cline, Roo Code, Zed, LM Studio and other clients

Any client that runs stdio MCP servers works with this command and environment variable:

• Command: npx -y hirelayer-mcp
• Environment: HIRELAYER_API_KEY=your-api-key

Cline users can also ask Cline to install the server: llms-install.md has the steps.

Docker

docker build -t hirelayer-mcp .

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

In Docker, parse_resume only reads files that you mount into the container. Otherwise, pass file_url or resume_text.

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[Short