Recruitee MCP Server

Provides advanced search, reporting, and analytics for recruitment data via Recruitee.

GitHub

Recruitee MCP Server

Model Context Protocol (MCP) server for Recruitee – advanced search, reporting, and analytics for recruitment data.

Deploy on Fly.io License: MIT

🚀 Overview

The Model Context Protocol (MCP) is rapidly becoming the standard for connecting AI agents to external services. This project implements an MCP server for Recruitee, enabling advanced, AI-powered search, filtering, and reporting on recruitment data.

Unlike basic CRUD wrappers, this server focuses on the tasks where LLMs and AI agents excel: summarizing, searching, and filtering. It exposes a set of tools and prompt templates, making it easy for any MCP-compatible client to interact with Recruitee data in a structured, agent-friendly way.

✨ Features

  • Advanced Candidate Search & Filtering
    Search for candidates by skills, status, talent pool, job, tags, and more. Example:
    "Find candidates with Elixir experience who were rejected due to salary expectations."

  • Recruitment Summary Reports
    Generate summaries of recruitment activities, such as time spent in each stage, total process duration, and stage-by-stage breakdowns.

  • Recruitment Statistics
    Calculate averages and metrics (e.g., average expected salary for backend roles, average time to hire, contract type stats).

  • General Search
    Quickly find candidates, recruitments, or talent pools by name or attribute.

  • Prompt Templates
    Exposes prompt templates for LLM-based clients, ensuring consistent and high-quality summaries.

🛠 Example Queries

  • Find candidates with Elixir experience who were rejected due to salary expectations.
  • Show me their personal details including CV URL.
  • Why was candidate 'X' disqualified and at what stage?
  • What are the other stages for this offer?
  • Show candidates whose GDPR certification expires this month.
  • What's time to fill sales assistant offer?
  • Create a pie chart with sources for AI engineer offer.
  • Create a recruitment report.

🧑‍💻 Implementation

The server retrieves and processes data from Recruitee, exposing it via MCP tools. Summaries are composed by the client using provided prompt templates.

🚦 Transport Methods

  • stdio – For local development and testing.
  • streamable-http – For remote, production-grade deployments (recommended).
  • SSE – Supported but deprecated in some MCP frameworks.

🧪 Usage

💡 Tip: For data visualization, combine this with chart-specific MCP servers like mcp-server-chart

Local (stdio)

  1. Configure your MCP client:

    {
  "mcpServers": {
    "recruitee": {
      "command": "/path/to/.venv/bin/python",
      "args": ["/path/to/recruitee-mcp-server/src/app.py", "--transport", "stdio"]
    }
  }
}

  2. Run with mcp-cli:

    mcp-cli chat --server recruitee --config-file /path/to/mcp-cli/server_config.json

Remote (streamable-http)

  1. Use mcp-remote:

    {
  "mcpServers": {
    "recruitee": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "https://recruitee-mcp-server.fly.dev/mcp/",
        "--header",
        "Authorization: Bearer ${MCP_BEARER_TOKEN}"
      ],
      "env": {
        "MCP_BEARER_TOKEN": "KEY"
      }
    }
  }
}

  2. or use directly if client supports bearer token authorization

    {
  "mcpServers": {
    "recruitee": {
      "transport": "streamable-http",
      "url": "https://recruitee-mcp-server.fly.dev/mcp"
    }
  }
}

☁️ Deployment

Deploy to Fly.io

  1. Set your secrets in .env

  2. Create a volume

    make create_volume

  3. Deploy:

    flyctl auth login
make deploy

📚 Resources

🤝 Contributing

Contributions, issues, and feature requests are welcome!

📝 License

This project is MIT licensed.

Empower your AI agents with advanced recruitment data access and analytics.

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