How to Use the Ninehire MCP in LlamaIndex

Index your entire recruitment history in LlamaIndex to query candidate profiles and job details using semantic search.

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Connect Ninehire MCP to LlamaIndex

Create your Vinkius account to connect Ninehire 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 Ninehire with LlamaIndex

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Query recruitment knowledge with LlamaIndex

Use `get_job_details` to pull specific requirements into your vector index. You get a searchable knowledge base that understands the nuances of your open roles. LlamaIndex turns the output of these tools into context for your queries. You stop guessing which candidates match a role and start searching your own data.

Index candidate evaluations for better insights

Pull data from `list_candidate_evaluations` and store it in your LlamaIndex knowledge graph. You can ask your agent to compare performance metrics across different hiring cycles. This creates a grounded history of your hiring decisions. You aren't relying on static reports; you're querying the actual outcomes of past evaluations.

Manage job postings through semantic search

Run `list_job_postings` to feed your index the latest information on active roles. Your agent can then answer questions about open positions based on the most recent data. This MCP Server makes your live Ninehire data part of your RAG application. You get answers based on current job statuses rather than stale documents.

Setup guide

Set up Ninehire 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 Ninehire 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 Ninehire tools.",
)
response = await agent.run("List recent Ninehire 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 Ninehire. 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 Ninehire MCP in LlamaIndex

Use the MCP tool spec to fetch data and pass the output to your ingestion pipeline. LlamaIndex will handle the chunking and embedding for your vector store.

Yes. You can pull the applicant list, index the results, and then perform semantic queries on that data. It works just like any other document source.

You control the refresh frequency. You can trigger tool calls periodically to pull the latest updates from the Ninehire API and update your index accordingly.

No. You can use any store supported by LlamaIndex. The MCP server just acts as the data provider for your retrieval process.

Your contact information is indexed locally within your chosen vector store. We don't have access to your embeddings or your private recruitment documents.

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