How to integrate Lever MCP with Mastra AI

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Introduction

This guide walks you through connecting Lever to Mastra AI 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 Mastra AI 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:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes Lever tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Lever tool definitions and attach them as a toolset
  • Build a Mastra agent that can reason, call tools, and return structured results
  • Run an interactive CLI where you can chat with your Lever agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

  • MCP Client: Built-in support for Model Context Protocol servers
  • Toolsets: Organize tools into logical groups
  • Step Callbacks: Monitor and debug agent execution
  • OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

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:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key.
  • You need credits or a connected billing setup to use the models.
  • Store the key somewhere safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Go to Settings and copy your API key.
  • This key lets your Mastra agent talk to Composio and reach Lever through MCP.

Install dependencies

bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

  • @composio/core is the Composio SDK for creating MCP sessions
  • @mastra/core provides the Agent class
  • @mastra/mcp is Mastra's MCP client
  • @ai-sdk/openai is the model wrapper for OpenAI
  • dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_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 your requests to Composio
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models

Import libraries and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey as string,
});
What's happening:
  • dotenv/config auto loads your .env so process.env.* is available
  • openai gives you a Mastra compatible model wrapper
  • Agent is the Mastra agent that will call tools and produce answers
  • MCPClient connects Mastra to your Composio MCP server
  • Composio is used to create a Tool Router session

Create a Tool Router session for Lever

typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["lever"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Lever MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "lever" for Lever access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to

Configure Mastra MCP client and fetch tools

typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
What's happening:
  • MCPClient takes an id for this client and a list of MCP servers
  • The headers property includes the x-api-key for authentication
  • getTools fetches the tool definitions exposed by the Lever toolkit

Create the Mastra agent

typescript
const agent = new Agent({
    name: "lever-mastra-agent",
    instructions: "You are an AI agent with Lever tools via Composio.",
    model: "openai/gpt-5",
  });
What's happening:
  • Agent is the core Mastra agent
  • name is just an identifier for logging and debugging
  • instructions guide the agent to use tools instead of only answering in natural language
  • model uses openai("gpt-5") to configure the underlying LLM

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

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

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();

rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        lever: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • messages keeps the full conversation history in Mastra's expected format
  • agent.generate runs the agent with conversation history and Lever toolsets
  • maxSteps limits how many tool calls the agent can take in a single run
  • onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

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

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({ apiKey: composioAPIKey as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["lever"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      lever: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "lever-mastra-agent",
    instructions: "You are an AI agent with Lever tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { lever: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Lever through Composio's Tool Router. You can extend this further by:
  • Adding other toolkits like Gmail, Slack, or GitHub
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows

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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 Mastra AI?

Yes, you can. Mastra AI 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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