recruitee-mcp-server by appunite - MCP Server

Recruitee MCP Server

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

🚀 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

Language: Python
Framework: FastMCP
API: Recruitee Careers Site API
Schemas: All MCP tool schemas are generated from Pydantic models, with rich metadata for LLMs.

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)

Configure your MCP client:

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

Run with mcp-cli:

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

Remote (streamable-http)

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"
      }
    }
  }
}

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

Set your secrets in .env
Create a volume
```
make create_volume
```
Deploy:
```
flyctl auth login
make deploy
```

📚 Resources

🤝 Contributing

Contributions, issues, and feature requests are welcome!

📝 License

This project is .

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