How to Use the Greenhouse Alternative MCP in LlamaIndex

Q: Can I perform semantic searches using Greenhouse Alternative and LlamaIndex?

Yes, by passing getcandidateactivityfeed to your LlamaIndex agent, you can index candidate interactions into a vector store. This lets you run semantic queries over historical recruiter notes and candidate histories directly.

Q: How do I avoid duplicate profiles using Greenhouse Alternative in LlamaIndex?

Use listapplications to retrieve current candidate records and index them in LlamaIndex. Before calling submitboardapplication, query your index to check if the applicant already exists in your pipeline.

Q: Can I filter which tools LlamaIndex can access?

Yes, you can use the allowedtools filter in McpToolSpec to limit LlamaIndex's access. For example, you can expose listboardjobs for public searches while hiding destructive tools like deleteapplication.

Q: How is candidate privacy maintained during indexing?

Only the text data extracted from tools like getapplication is sent to your embedding model. Your Greenhouse API keys are managed securely in Vinkius, ensuring candidate application files are never exposed to unauthorized external networks.

Index live recruiting data and query your Greenhouse Alternative pipeline directly from LlamaIndex RAG applications.

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Connect Greenhouse Alternative MCP to LlamaIndex

Create your Vinkius account to connect Greenhouse Alternative 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 Alternative with LlamaIndex

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Turn candidate feeds into a queryable LlamaIndex RAG

This Greenhouse Alternative MCP server allows LlamaIndex to index your live recruitment pipeline for semantic search. By pulling candidate history with `get_candidate_activity_feed`, the framework builds vector embeddings of candidate notes, making past interview feedback searchable. Instead of manual keyword searching, your agent queries the index to find past candidates with specific skills. It uses `list_applications` to match historical profiles against open roles, grounding its recommendations in your actual ATS database.

Build real-time job board search engines

This toolset lets your LlamaIndex agent fetch and index active job listings on demand. The agent calls `list_board_jobs` and `get_board_job` to extract application questions, converting them into structured documents for your vector store. This means your internal search tools can query departmental requirements via `list_board_departments` and office locations with `list_board_offices` instantly. Candidates get highly accurate, real-time job matches based on live board data.

Automate partner candidates with semantic validation

This server enables LlamaIndex agents to validate incoming partner submissions before they hit your database. When a partner sends a profile, the agent uses `get_partner_candidates` to check for duplicates and assess semantic fit against current open jobs. Once validated, the agent executes `submit_partner_candidate` or updates an existing record using `update_application`. This keeps your partner pipeline clean and prevents duplicate candidate profiles from polluting your ATS.

Setup guide

Set up Greenhouse Alternative 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 Alternative 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 Alternative tools.",
)
response = await agent.run("List recent Greenhouse Alternative data")

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Common questions about Greenhouse Alternative MCP in LlamaIndex

Yes, by passing `get_candidate_activity_feed` to your LlamaIndex agent, you can index candidate interactions into a vector store. This lets you run semantic queries over historical recruiter notes and candidate histories directly.

The MCP server itself does not store vector data. It provides the raw candidate data via tools like `get_application`, which LlamaIndex then parses, embeds, and loads into your chosen vector database.

Use `list_applications` to retrieve current candidate records and index them in LlamaIndex. Before calling `submit_board_application`, query your index to check if the applicant already exists in your pipeline.

Yes, you can use the `allowed_tools` filter in `McpToolSpec` to limit LlamaIndex's access. For example, you can expose `list_board_jobs` for public searches while hiding destructive tools like `delete_application`.

Only the text data extracted from tools like `get_application` is sent to your embedding model. Your Greenhouse API keys are managed securely in Vinkius, ensuring candidate application files are never exposed to unauthorized external networks.

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