Can my agent list all candidates for a specific job in Greenhouse?

Yes. Use the 'list_candidates' tool with the 'job_id' parameter. The agent will fetch all candidates associated with that specific opening flawlessly.

How do I check the current hiring stage of an applicant via chat?

You can use the 'get_application' tool. Provide the Application ID, and the agent will return the full details, including the candidate's current stage in the hiring process natively.

Can I search for candidates who were added recently through the agent?

Absolutely. Use the 'list_candidates' tool with sorting or filtering parameters like 'created_after'. Your agent will retrieve the most recent additions to your Greenhouse database natively.

Greenhouse MCP Server for LlamaIndex 12 tools — connect in under 2 minutes

Name: Greenhouse
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 12 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Greenhouse as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Greenhouse. "
            "You have 12 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Greenhouse?"
    )
    print(response)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Greenhouse MCP Server

Connect your Greenhouse Recruiting account to any AI agent and take control of your talent acquisition pipeline through natural conversation.

LlamaIndex agents combine Greenhouse tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Candidate Oversight — List all candidates in your system and retrieve specific profile details natively
Pipeline Tracking — Monitor job applications and their current statuses across your active hiring processes flawlessly
Job Management — List and inspect job configurations, including hiring stages and department mappings synchronously
Team Coordination — Retrieve office and department structures to ensure your hiring data is aligned with organizational goals
User Auditing — List and verify user roles and access levels within your Greenhouse workspace natively

The Greenhouse MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Greenhouse to LlamaIndex via MCP

Follow these steps to integrate the Greenhouse MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 12 tools from Greenhouse

Why Use LlamaIndex with the Greenhouse MCP Server

LlamaIndex provides unique advantages when paired with Greenhouse through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Greenhouse tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Greenhouse tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Greenhouse, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Greenhouse tools were called, what data was returned, and how it influenced the final answer

Greenhouse + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Greenhouse MCP Server delivers measurable value.

Hybrid search: combine Greenhouse real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Greenhouse to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Greenhouse for fresh data

Analytical workflows: chain Greenhouse queries with LlamaIndex's data connectors to build multi-source analytical reports

Greenhouse MCP Tools for LlamaIndex (12)

These 12 tools become available when you connect Greenhouse to LlamaIndex via MCP:

create_candidate

Create a new candidate profile

get_application

Get details for a specific application

get_candidate

Get details for a specific candidate

get_job

Get details for a specific job

get_user

Get details for a specific user

list_applications

Retrieve job applications

list_candidates

List all candidates in Greenhouse

list_departments

List company departments

list_job_stages

List hiring stages for a specific job

list_jobs

List jobs in Greenhouse

list_offices

List company offices

list_users

List Greenhouse users

Example Prompts for Greenhouse in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Greenhouse immediately.

"List my active jobs in Greenhouse"

"Show me the profile for candidate ID 93021"

"What are the hiring stages for the 'Product Designer' job?"

Troubleshooting Greenhouse MCP Server with LlamaIndex

Common issues when connecting Greenhouse to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Greenhouse + LlamaIndex FAQ

Common questions about integrating Greenhouse MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Greenhouse tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.