How to Use the Greenhouse MCP in LlamaIndex

Q: Can I search Greenhouse candidates using LlamaIndex semantic search?

Yes. Use listcandidates to pull applicant profiles, index them into your vector store, and run natural language queries to find matching talent.

Q: How does LlamaIndex handle Greenhouse API rate limits?

The Greenhouse MCP Server handles connection health via getapistatus. LlamaIndex queries the server, which manages the raw API calls and returns structured data for indexing.

Q: Can LlamaIndex automatically move Greenhouse candidates to the next stage?

Yes, your LlamaIndex agent uses advanceapplication to progress candidates once they meet the index's evaluation thresholds. This keeps your pipeline moving based on structured data.

Q: How do I list active job openings in LlamaIndex?

Call listopenjobs to ingest active listings into your index. Your LlamaIndex agent can then compare candidate profiles against these live roles to find the best fit.

Index your Greenhouse recruiting data directly into LlamaIndex to query candidate pipelines with natural language using this MCP Server.

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Connect Greenhouse MCP to LlamaIndex

Create your Vinkius account to connect Greenhouse to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Greenhouse with LlamaIndex

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Index Live Candidate Data with LlamaIndex

The `list_candidates` tool fetches active Greenhouse applicants so your LlamaIndex pipeline can index their resumes and profiles into a vector store. This lets you run LlamaIndex semantic searches over your entire Greenhouse talent pool to find past applicants who fit new roles. Your LlamaIndex agent uses `get_candidate_details` to pull deep Greenhouse profile data, turning static resumes into queryable knowledge. You can ask LlamaIndex who has specific engineering experience without writing complex SQL queries against Greenhouse.

Query Job Requirements and Match Applicants

The `get_job_details` tool extracts Greenhouse hiring criteria and role metadata directly into your LlamaIndex knowledge base. This grounds your agent's search, ensuring it matches candidates based on actual team requirements. Your LlamaIndex agent queries this index alongside `list_open_jobs` to identify which active Greenhouse listings are getting the right applicants. This connects live Greenhouse pipeline metrics directly to your planning documents inside LlamaIndex.

Update Pipelines directly from LlamaIndex MCP Server

The `update_candidate` tool modifies Greenhouse applicant profiles based on insights generated during your LlamaIndex semantic search queries. When LlamaIndex finds a match, it updates the Greenhouse candidate's tags or custom fields to flag them for review. Your LlamaIndex agent can then run `advance_application` to push qualified Greenhouse matches into the active interview queue. This turns your static Greenhouse talent database into an active, self-sorting pipeline managed by LlamaIndex.

Setup guide

Set up Greenhouse MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Greenhouse MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Greenhouse tools.",
)
response = await agent.run("List recent Greenhouse data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Greenhouse. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Greenhouse MCP in LlamaIndex

Yes. Use `list_candidates` to pull applicant profiles, index them into your vector store, and run natural language queries to find matching talent.

The Greenhouse MCP Server handles connection health via `get_api_status`. LlamaIndex queries the server, which manages the raw API calls and returns structured data for indexing.

Yes, your LlamaIndex agent uses `advance_application` to progress candidates once they meet the index's evaluation thresholds. This keeps your pipeline moving based on structured data.

Call `list_open_jobs` to ingest active listings into your index. Your LlamaIndex agent can then compare candidate profiles against these live roles to find the best fit.

All candidate profiles and application statuses processed by this MCP Server remain in your private vector index. Vinkius operates a zero-trust environment, ensuring no recruiting data is stored or logged.

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