| 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
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",
});
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 }) => {
const response = await fetch(`${BASE_URL}/v1/candidates`, {
headers: { "Authorization": `Bearer ${API_KEY}` },
});
const data = await response.json();
return {
: [{ : , : .(data, , ) }],
};
}
);
server.(
,
,
{
: z.().(),
: z.({}).().().(),
},
({ name, config }) => {
response = (, {
: ,
: {
: ,
: ,
},
: .({ name, ...config }),
});
data = response.();
{
: [{ : , : }],
};
}
);
server.(
,
,
{
: z.().(),
: z.().().(),
},
({ , timeframe }) => {
response = (, {
: { : },
});
data = response.();
{
: [{ : , : .(data, , ) }],
};
}
);
transport = ();
server.(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
- Authentication: Store API keys securely using environment variables; never hardcode credentials
- Rate Limiting: Implement request throttling to respect Greenhouse API rate limits
- Error Handling: Provide clear, actionable error messages for common failure scenarios
- Pagination: Handle paginated responses for large datasets efficiently
- Caching: Cache frequently accessed read-only data to reduce API calls
- Security: Validate all inputs before passing to the Greenhouse API; sanitize outputs
- 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
Changelog
| Version | Date | Changes |
|---|
| 1.0.0 | 2026-04-01 | Initial MCP connector skill |
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