Lever MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Lever as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Lever. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Lever?"
)
print(response)
asyncio.run(main())
* 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 Lever MCP Server
Connect your Lever account to any AI agent to streamline your recruitment and talent acquisition workflows. This MCP server enables your agent to interact with job postings, manage candidate opportunities, and move applications through your hiring pipeline directly.
LlamaIndex agents combine Lever tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Posting Oversight — List and retrieve detailed configurations for all your active job advertisements
- Opportunity Management — Manage candidate applications (Opportunities), track their status, and move them through hiring stages
- Candidate Insight — Access complete candidate profiles, contact details, and interaction histories
- Pipeline Control — List hiring stages and automate the archiving of applications with specific reasons
- Workflow Automation — Create new job postings or candidate records directly from natural language interfaces
The Lever MCP Server exposes 10 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 Lever to LlamaIndex via MCP
Follow these steps to integrate the Lever 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 10 tools from Lever
Why Use LlamaIndex with the Lever MCP Server
LlamaIndex provides unique advantages when paired with Lever through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Lever tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Lever tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Lever, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Lever tools were called, what data was returned, and how it influenced the final answer
Lever + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Lever MCP Server delivers measurable value.
Hybrid search: combine Lever real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Lever 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 Lever for fresh data
Analytical workflows: chain Lever queries with LlamaIndex's data connectors to build multi-source analytical reports
Lever MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Lever to LlamaIndex via MCP:
archive_hiring_opportunity
Archive a candidate opportunity
create_hiring_opportunity
Requires a JSON body with opportunity details. Create a new candidate opportunity
create_job_posting
Requires a JSON body with posting details. Create a new job posting
get_candidate_profile
Get details for a specific candidate (person)
get_opportunity_details
Get details for a specific candidate opportunity
get_posting_details
Get details for a specific job posting
list_hiring_opportunities
List all candidate opportunities (applications)
list_hiring_stages
g., Screen, Interview) configured in your Lever account. List all defined hiring pipeline stages
list_job_postings
List all job postings
update_opportunity_stage
g., move to "Interview" or "Offer"). Move a candidate to a different hiring stage
Example Prompts for Lever in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Lever immediately.
"List all my current job postings in Lever."
"Move opportunity ID 'opp-123' to the 'Interview' stage (ID: 'stage-abc')."
"Get the full profile for candidate ID 'cand-987'."
Troubleshooting Lever MCP Server with LlamaIndex
Common issues when connecting Lever to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLever + LlamaIndex FAQ
Common questions about integrating Lever MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Lever with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Lever to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.