greenhouse-mcp-connector

name	greenhouse-mcp-connector
description	MCP connector for Greenhouse recruiting – enables AI-powered hiring pipeline management, candidate evaluation, and interview coordination through Claude
license	Proprietary/API
tags	["business","hr","mcp","greenhouse","recruiting"]
difficulty	intermediate
time_to_master	4-8 weeks
version	1.0.0

Greenhouse MCP Connector

Overview

This skill enables Claude to interact with Greenhouse through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Greenhouse's REST API, allowing natural language control of Greenhouse operations, intelligent automation, and AI-powered assistance for Greenhouse workflows.

When to Use This Skill

• Candidate screening and evaluation summaries
• Interview scorecard analysis
• Job posting creation and optimization
• Pipeline stage analytics and reporting
• Offer letter generation and management

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Greenhouse       │
│   (Client)   │◀────│   (TypeScript)  │◀────│   (REST API      )│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Greenhouse operations as tools Claude can invoke. The server translates natural language intentions into REST API calls.

Key Endpoints/Interfaces

/v1/candidates, /v1/applications, /v1/jobs, /v1/offers, /v1/scorecards

Implementation

// Greenhouse MCP Server Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "greenhouse-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Greenhouse resources with optional filters",
  {
    query: z.string().optional().describe("Search query or filter"),
    limit: z.number().optional().describe("Max results to return"),
  },
  async ({ query, limit }) => {
    // Call Greenhouse REST API
    const response = await fetch(`${BASE_URL}/v1/candidates`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Tool: Create Resource
server.tool(
  "create_resource",
  "Create a new resource in Greenhouse",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}/v1/candidates`, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${API_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ name, ...config }),
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: `Created: ${JSON.stringify(data)}` }],
    };
  }
);

// Tool: Analyze/Report
server.tool(
  "analyze",
  "AI-powered analysis of Greenhouse data",
  {
    type: z.string().describe("Analysis type"),
    timeframe: z.string().optional().describe("Time range for analysis"),
  },
  async ({ type, timeframe }) => {
    // Fetch data and provide AI analysis
    const response = await fetch(`${BASE_URL}/v1/candidates`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Claude Desktop Configuration

{
  "mcpServers": {
    "greenhouse-mcp-connector": {
      "command": "node",
      "args": ["path/to/greenhouse-mcp-connector/index.js"],
      "env": {
        "GREENHOUSE_API_KEY": "your-api-key",
        "GREENHOUSE_BASE_URL": "https://your-instance-url"
      }
    }
  }
}

Best Practices

1. Authentication: Store API keys securely using environment variables; never hardcode credentials
2. Rate Limiting: Implement request throttling to respect Greenhouse API rate limits
3. Error Handling: Provide clear, actionable error messages for common failure scenarios
4. Pagination: Handle paginated responses for large datasets efficiently
5. Caching: Cache frequently accessed read-only data to reduce API calls
6. Security: Validate all inputs before passing to the Greenhouse API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Summarize all candidates in the final interview stage and compare their scorecard ratings"

Security Considerations

• All API credentials must be stored as environment variables
• Implement input validation and sanitization for all tool parameters
• Use HTTPS for all API communications
• Follow the principle of least privilege for API token permissions
• Audit log all write operations for compliance tracking

Resources

• Greenhouse Official Documentation
• MCP SDK Documentation: https://modelcontextprotocol.io
• MCP Server Examples: https://github.com/modelcontextprotocol/servers
• SkillGalaxy Repository: https://github.com/Sandeeprdy1729/skill_galaxy

Changelog

Version	Date	Changes
1.0.0	2026-04-01	Initial MCP connector skill

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