How to Use the Lever MCP in LlamaIndex

Q: How do I configure the Lever MCP Server with LlamaIndex?

Install llama-index-tools-mcp and initialize BasicMCPClient with your server URL. Wrap it in a McpToolSpec and call totoollistasync() to get the tools for your FunctionAgent.

Q: Can LlamaIndex query candidate profiles from Lever?

Yes. The agent uses getcandidateprofile to pull specific candidate records, which LlamaIndex then indexes on the fly for your semantic search queries.

Q: Can I restrict which MCP tools are exposed to the agent?

Yes. You can use the allowedtools filter when setting up your tool specification, ensuring your agent only has access to read-only tools if you want to prevent automated updates.

Index Lever candidate data directly into LlamaIndex vector stores to build search engines for your hiring pipeline.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Lever MCP to LlamaIndex

Create your Vinkius account to connect Lever 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.

GDPR Free for Subscribers

Setup Lever with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Grounding LlamaIndex RAG in live hiring data

This MCP Server exposes `list_hiring_opportunities` and `get_opportunity_details` to feed your LlamaIndex vector store with active application data. The framework reads the tool outputs and indexes them as document nodes. This prevents your query engine from making up details about candidate progress. When you run a query about your hiring pipeline, LlamaIndex pulls the latest records directly from the server. Your agent answers questions using real-time data instead of relying on stale offline exports.

Indexing job postings for semantic search

You can use `list_job_postings` and `get_posting_details` to build a semantic search index of all open roles inside LlamaIndex using this MCP Server. Your application can match incoming resumes against these indexed postings to find the best fit. This bypasses basic keyword matching in favor of actual semantic understanding. The indexing pipeline updates automatically as new postings are published. This ensures your search engine always references active requirements without manual re-indexing runs.

Pipeline stage analysis

The `list_hiring_stages` tool allows your LlamaIndex agent to map the structure of your recruiting funnel. By combining stage structures with candidate opportunities, the agent builds a clear picture of bottleneck locations. It indexes these patterns to help you analyze where candidates stall. Because LlamaIndex supports structured data indexing, it organizes these stages into a queryable graph. You can ask your agent which stages have the longest delays and get a data-backed response instantly.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Lever. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Lever now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Lever MCP in LlamaIndex

Install `llama-index-tools-mcp` and initialize `BasicMCPClient` with your server URL. Wrap it in a `McpToolSpec` and call `to_tool_list_async()` to get the tools for your `FunctionAgent`.

Yes. The agent uses `get_candidate_profile` to pull specific candidate records, which LlamaIndex then indexes on the fly for your semantic search queries.

LlamaIndex relies on the underlying client to manage execution frequency. When indexing bulk listings via `list_job_postings`, implement a rate-limiting wrapper to avoid hitting API limits.

Yes. You can use the `allowed_tools` filter when setting up your tool specification, ensuring your agent only has access to read-only tools if you want to prevent automated updates.

Yes. The server processes your job postings and candidate profiles locally within an isolated V8 sandbox. No data is stored on Vinkius, and all communication between LlamaIndex and the server is encrypted.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript