How to integrate Lever MCP with CrewAI

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Introduction

This guide walks you through connecting Lever to CrewAI using the Composio tool router. By the end, you'll have a working Lever agent that can list all open job postings, get candidate details by email, schedule interview for specific candidate through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Lever account through Composio's Lever MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Lever with

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Lever connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Lever
  • Build a conversational loop where your agent can execute Lever operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

What is the Lever MCP server, and what's possible with it?

The Lever MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Lever account. It provides structured and secure access to your recruiting pipeline, so your agent can perform actions like managing candidates, scheduling interviews, updating job postings, tracking offers, and analyzing hiring metrics on your behalf.

  • End-to-end candidate management: Let your agent add, update, or move candidates through different stages of your hiring process seamlessly.
  • Automated interview scheduling: Have the agent create, modify, or cancel interviews and coordinate with both candidates and interviewers to streamline the process.
  • Job posting and requisition updates: Direct your agent to create new job postings, update existing requisitions, or close filled roles instantly.
  • Offer and feedback tracking: Enable your agent to manage offer letters, track acceptance rates, and collect structured feedback from interviewers.
  • Recruiting analytics and reporting: Ask the agent to generate reports on pipeline activity, source effectiveness, and diversity metrics—helping you make data-driven hiring decisions.

Supported Tools & Triggers

Tools
Add Opportunity LinksTool to add links to a contact associated with an opportunity.
Add Opportunity SourcesTool to add sources to an opportunity.
Add Opportunity TagsTool to add tags to an opportunity.
Create Form SubmissionTool to create a completed profile form submission for a candidate's opportunity profile.
Create Form TemplateTool to create a profile form template for an account.
Create InterviewTool to create an interview on an externally-managed panel in Lever.
Create NoteTool to create a note on an opportunity profile or add a threaded comment to an existing note.
Create OpportunityTool to create a new candidate opportunity in Lever.
Create PanelTool to create a new interview panel for an opportunity.
Create RequisitionTool to create a new requisition in Lever for tracking hiring needs.
Create Requisition FieldTool to create a custom requisition field schema for use across requisitions.
Create Requisition Field OptionTool to add new options to a dropdown requisition field without replacing existing options.
Upload FileTool to upload a file temporarily to Lever for use with posting applications.
Create UserTool to create a new user in the Lever system.
Deactivate UserTool to deactivate a user in the Lever system.
Delete Form TemplateTool to delete a profile form template from account.
Delete InterviewTool to delete an interview from an opportunity panel.
Delete NoteTool to delete a note on an opportunity.
Delete PanelTool to delete a panel from an opportunity.
Delete RequisitionTool to delete or archive a requisition from Lever account.
Delete Requisition FieldTool to delete a requisition field from the account.
Delete Requisition Field OptionTool to remove specific options from a dropdown requisition field.
Download FileTool to download a file associated with an opportunity.
Get File MetadataTool to retrieve metadata for a single file on an opportunity.
Get FormTool to retrieve a specific profile form for an opportunity.
Get Form TemplateTool to retrieve a single form template by unique identifier.
Get InterviewTool to retrieve a single interview for an opportunity.
Get NoteTool to retrieve a single note for an opportunity.
Get OpportunityTool to retrieve detailed information about a single opportunity.
Get PanelTool to retrieve a single interview panel for an opportunity.
Get RequisitionTool to retrieve detailed information about a single requisition by ID.
Get Requisition FieldTool to retrieve detailed information about a single custom requisition field by ID.
Get StageTool to retrieve detailed information about a single stage by its UUID.
Get UserTool to retrieve detailed information about a single user by their UUID.
List Opportunity FilesTool to list all files on an opportunity.
List FormsTool to list all profile forms for an opportunity.
List Form TemplatesTool to list all active form templates.
List InterviewsTool to list all interviews for an opportunity.
List NotesTool to list notes on an opportunity profile.
List OffersTool to list offers for an opportunity.
List OpportunitiesTool to list all opportunities in the hiring pipeline.
List PanelsTool to list all interview panels for an opportunity.
List PostingsTool to list all job postings including published, internal, closed, draft, pending, and rejected postings.
List ReferralsTool to list all referrals for an opportunity.
List Requisition FieldsTool to list all requisition field schemas in your Lever account with optional filtering.
List RequisitionsTool to list all requisitions with filtering and pagination.
List Opportunity ResumesTool to list all resumes for an opportunity.
List SourcesTool to list all recruitment sources in your Lever account.
List StagesTool to retrieve all pipeline stages in your Lever account.
List TagsTool to list all tags in your Lever account.
List UsersTool to retrieve all active users in your Lever account with optional filters.
Reactivate UserTool to reactivate a previously deactivated user in the Lever system.
Remove Contact Links by OpportunityTool to remove links from a contact associated with an opportunity.
Remove Opportunity SourcesTool to remove sources from an opportunity.
Remove Opportunity TagsTool to remove tags from an opportunity.
Update Form TemplateTool to update an existing profile form template.
Update InterviewTool to update an interview on an externally-managed panel.
Update NoteTool to update a note on an opportunity profile.
Update PanelTool to update an externally-managed panel for an opportunity.
Update RequisitionTool to update an existing requisition in Lever.
Update Requisition FieldTool to update an existing requisition field in Lever.
Update Requisition Field OptionTool to update existing options in a dropdown requisition field without replacing the entire field object.
Update UserTool to update an existing user in the Lever system.
Upload File to OpportunityTool to upload a file permanently to an opportunity.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Lever connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.

Install dependencies

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Lever via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Lever MCP URL

Create a Composio Tool Router session for Lever

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["lever"])

url = session.mcp.url
What's happening:
  • You create a Lever only session through Composio
  • Composio returns an MCP HTTP URL that exposes Lever tools

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's Happening:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

Here's the complete code to get you started with Lever and CrewAI:

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["lever"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Lever through Composio's Tool Router. The agent can perform Lever operations through natural language commands.

Next steps:

  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

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FAQ

What are the differences in Tool Router MCP and Lever MCP?

With a standalone Lever MCP server, the agents and LLMs can only access a fixed set of Lever tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Lever and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with CrewAI?

Yes, you can. CrewAI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Lever tools.

Can I manage the permissions and scopes for Lever while using Tool Router?

Yes, absolutely. You can configure which Lever scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Lever data and credentials are handled as safely as possible.

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