How do I move a candidate to a specific stage?

Use the `update_opportunity_stage` tool with the unique opportunity ID and the target stage ID. You can find stage IDs using the `list_hiring_stages` tool.

Can I archive an application via the agent?

Yes, use the `archive_hiring_opportunity` tool. You will need to provide the opportunity ID and a valid archive reason ID.

Is it possible to see a candidate's contact information?

Absolutely. Use the `get_candidate_profile` tool with the candidate's unique ID to retrieve their full profile, including email, phone, and links.

Lever MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Lever
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Lever through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "lever": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Lever, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

LangChain's ecosystem of 500+ components combines seamlessly with Lever through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the Lever MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Lever via MCP

Why Use LangChain with the Lever MCP Server

LangChain provides unique advantages when paired with Lever through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Lever MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Lever queries for multi-turn workflows

Lever + LangChain Use Cases

Practical scenarios where LangChain combined with the Lever MCP Server delivers measurable value.

RAG with live data: combine Lever tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Lever, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Lever tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Lever tool call, measure latency, and optimize your agent's performance

Lever MCP Tools for LangChain (10)

These 10 tools become available when you connect Lever to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

Common issues when connecting Lever to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Lever + LangChain FAQ

Common questions about integrating Lever MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.