🎯 AI Candidate Screener — Model Context Protocol (MCP) Server

An autonomous, evidence-driven Model Context Protocol (MCP) Server for technical recruitment and ATS resume screening (default: Senior SDET / QA Automation, extensible to any Job Description).

Connect this MCP server to Google Antigravity IDE, VS Code (GitHub Copilot / Cline / Cursor / Roo-Code), or Claude Desktop to empower your AI assistant to parse multi-format resumes, eliminate keyword inflation, and generate audit-ready gap matrices.

✨ Features & Capabilities

• Strict "No-Skill-Inflation" 4-Tier Verification Engine:

  • 🟢 Matched (1.00× weight): Verified in $\ge 1$ concrete project deliverable.

  • 🟠 Partial Match (0.60× weight): Adjacent tech or minimal exposure.

  • 🔵 Claimed not evidenced (0.30× weight): Keyword list only, no proof.

  • 🔴 Missing (0.00× weight): Not found in resume.

• 100-Point Capacity Rubric: Evaluates candidates across Mandatory Skills (85 pts), Good-to-Have Bonus (10 pts), and Experience Fit (5 pts).

• Hard-Fail Rule Overrides: Automatically catches core language gaps and AI/Agentic testing gaps.

• Multi-Transport Support: Runs as a local stdio server or remote sse (Server-Sent Events) HTTP service.

• Cross-Platform Compatibility: Works on Windows, macOS, Linux, and Cloud (Render, Docker, Cloudflare, Hugging Face).

Related MCP server: resume-scorer-mcp

🛠️ MCP Tools, Resources & Prompts

🧰 Tools

📚 Resources

💬 Prompts

🚀 Quick Setup Guide

1. Installation

Clone this repository and install dependencies:

git clone https://github.com/surendra1220/candidate-screener-mcp.git
cd candidate-screener-mcp
pip install -r requirements.txt

🔌 How to Add to Your AI Tools

A. Google Antigravity IDE

Add to your Antigravity configuration file (~/.gemini/config/mcp_config.json):

{
  "mcpServers": {
    "candidate-screener": {
      "command": "python",
      "args": [
        "/path/to/candidate-screener-mcp/mcp_server.py"
      ]
    }
  }
}

(On Windows, use py with args ["-3", "C:\\path\\to\\candidate-screener-mcp\\mcp_server.py"])

Alternatively, in Antigravity IDE:

1. Click Additional Options (...) in the top right.

2. Select MCP Servers $\rightarrow$ Add Server.

3. Paste the configuration above.

B. Visual Studio Code (Copilot Agent Mode / Cline / Roo-Code / Cursor)

Add to your project's .vscode/mcp.json:

{
  "mcpServers": {
    "candidate-screener": {
      "command": "python",
      "args": [
        "${workspaceFolder}/mcp_server.py"
      ]
    }
  }
}

C. Claude Desktop

Add to your Claude Desktop configuration file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "candidate-screener": {
      "command": "python",
      "args": [
        "C:\\path\\to\\candidate-screener-mcp\\mcp_server.py"
      ]
    }
  }
}

🌐 Running as a Public / Remote SSE Server

You can also host the MCP server as a public HTTP SSE endpoint for team use:

Run Locally / Cloud Server:

python mcp_server.py --transport sse --host 0.0.0.0 --port 8000

Connect via Remote SSE in any MCP Client:

{
  "mcpServers": {
    "candidate-screener-remote": {
      "serverUrl": "https://your-domain.com/sse"
    }
  }
}

💡 Example Prompts to Ask Your AI Assistant

Once connected, you can interact with the Candidate Screener naturally:

1. Screen an uploaded candidate:

   "Using candidate-screener, screen the attached resume against our default SDET job description."

2. Screen with a custom role:

   "Evaluate this candidate against the custom JD provided in this prompt using the 4-tier no-inflation engine."

3. Inspect the active rubric:

   "What are the mandatory skill weights and AI hard-fail override rules in the active rubric?"

📦 Project Structure

candidate-screener-mcp/
├── mcp_server.py             # Main FastMCP Server (Tools, Resources, Prompts)
├── requirements.txt          # Python dependencies (mcp, pypdf, python-docx, fpdf2)
├── references/
│   ├── job-description.md    # Ground-truth SDET Job Description
│   └── rubric.md             # 100-point capacity scoring model & rules
├── .vscode/
│   └── mcp.json              # VS Code MCP configuration
├── antigravity_mcp_config.json # Antigravity IDE configuration
├── Dockerfile                # Container deployment configuration
└── README.md                 # Public documentation

📄 License

This project is open source and available under the MIT License.