# How to integrate Conveyor MCP with Mastra AI

```json
{
  "title": "How to integrate Conveyor MCP with Mastra AI",
  "toolkit": "Conveyor",
  "toolkit_slug": "conveyor",
  "framework": "Mastra AI",
  "framework_slug": "mastra-ai",
  "url": "https://composio.dev/toolkits/conveyor/framework/mastra-ai",
  "markdown_url": "https://composio.dev/toolkits/conveyor/framework/mastra-ai.md",
  "updated_at": "2026-05-06T08:07:22.514Z"
}
```

## Introduction

This guide walks you through connecting Conveyor to Mastra AI using the Composio tool router. By the end, you'll have a working Conveyor agent that can list all pending authorization requests, fetch all documents in your trust center, delete a folder by its id through natural language commands.
This guide will help you understand how to give your Mastra AI agent real control over a Conveyor account through Composio's Conveyor MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Conveyor with

- [OpenAI Agents SDK](https://composio.dev/toolkits/conveyor/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/conveyor/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/conveyor/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/conveyor/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/conveyor/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/conveyor/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/conveyor/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/conveyor/framework/cli)
- [Google ADK](https://composio.dev/toolkits/conveyor/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/conveyor/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/conveyor/framework/ai-sdk)
- [LlamaIndex](https://composio.dev/toolkits/conveyor/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/conveyor/framework/crew-ai)

## 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 Conveyor tools
- Connect Mastra's MCP client to the Composio generated MCP URL
- Fetch Conveyor 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 Conveyor 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 Conveyor MCP server, and what's possible with it?

The Conveyor MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Conveyor account. It provides structured and secure access to your security reviews and compliance workflows, so your agent can perform actions like retrieving documents, managing authorization requests, tracking connections, and automating security questionnaire processes on your behalf.
- Authorization request management: Fetch, list, and review details of all authorization requests, making it easy for your agent to help you track and respond to security and compliance requests in real time.
- Document and folder automation: Retrieve, organize, or delete specific documents and folders, ensuring your Trust Center stays tidy and up to date without manual effort.
- Connection insights and tracking: Access a complete list of your Conveyor connections, letting your agent monitor integrations and stay on top of your security ecosystem.
- Interaction history by document: Instantly pull all interactions related to a specific document, so your agent can summarize or audit user activity for compliance needs.
- API token validation and guidance: Use AI-driven guidance to validate API tokens and get structured support for access issues, helping keep your Conveyor integration secure and running smoothly.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `CONVEYOR_DELETE_DOCUMENT` | Delete a Conveyor document | Tool to delete a specific document. Use when you need to remove a document by its ID. |
| `CONVEYOR_DELETE_FOLDER` | Delete folder | Tool to delete a folder by its ID. Use when you need to remove a specific folder after confirming its ID. |
| `CONVEYOR_GENERATE_API_TOKEN` | Generate/Validate API Token Guidance | Tool to validate API token and provide guidance. Conveyor does not support API-based token creation; tokens must be created in the Conveyor UI. This action performs a real API call (using the provided metadata) to validate the existing API token and returns structured guidance. |
| `CONVEYOR_GET_AUTHORIZATION_REQUEST` | Get Authorization Request | Tool to fetch details of a specific authorization request. Use when you need to retrieve metadata by authorization_request_id. |
| `CONVEYOR_GET_AUTHORIZATION_REQUESTS` | Get Authorization Requests | Tool to fetch authorization requests. Use when you need to list authorization requests, optionally filtered by status. |
| `CONVEYOR_GET_AUTHORIZATIONS` | Get all authorization requests | Tool to retrieve all authorization requests. Use when you need to list all authorizations; optionally filter by status. Use after authenticating with a valid API token. |
| `CONVEYOR_GET_CONNECTIONS` | Get all Conveyor connections | Tool to retrieve all connections. Use when you need to fetch the complete list of your Conveyor connections. Use after authenticating with a valid API key. |
| `CONVEYOR_GET_DOCUMENTS` | Get all Conveyor documents | Tool to retrieve all documents. Use after authenticating with a valid API key. |
| `CONVEYOR_GET_FOLDERS` | Get all Conveyor folders | Tool to retrieve all folders. Use after authenticating with a valid API key to fetch the complete list of your Conveyor folders. |
| `CONVEYOR_GET_INTERACTIONS_BY_DOCUMENT_ID` | Get interactions by document ID | Tool to fetch interactions associated with a specific document. Use when you need to list all interactions for a given document after validating its existence. |
| `CONVEYOR_GET_KNOWLEDGE_BASE_QUESTIONS` | Get Knowledge Base Questions | Tool to retrieve knowledge base questions. Use when you need to fetch all questions from the Conveyor knowledge base. |
| `CONVEYOR_GET_PRODUCT_LINES` | Get product lines | Tool to fetch all product lines. Use when you need to retrieve product lines after confirming API key validity. |
| `CONVEYOR_PATCH_AUTHORIZATION` | Patch authorization | Tool to update or revoke an existing authorization. Use when managing authorization access groups or revoking access. |
| `CONVEYOR_PATCH_DOCUMENT` | Patch Conveyor document | Tool to update document attributes. Use when you need to modify fields of an existing document by its ID. |
| `CONVEYOR_POST_AUTHORIZATION` | Create new authorization | Tool to create a new authorization. Use when you need to grant access by email or from a prior authorization request. |
| `CONVEYOR_POST_DOCUMENT` | Upload new document | Tool to upload a new document. Use when you have a local file (<=100MB) to send to Conveyor. |
| `CONVEYOR_POST_FOLDER` | Create new folder | Tool to create a new folder in Conveyor Exchange. Use when you need to organize items into folders programmatically after obtaining an API key. |
| `CONVEYOR_POST_SINGLE_QUESTION` | Submit single question | Tool to submit a single question. Use when you need an immediate AI-generated answer for a specific product line question. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Conveyor MCP server is an implementation of the Model Context Protocol that connects your AI agent to Conveyor. It provides structured and secure access so your agent can perform Conveyor operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. 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

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Go to Settings and copy your API key.
- This key lets your Mastra agent talk to Composio and reach Conveyor through MCP.

### 2. Install dependencies

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

### 3. Set up environment variables

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
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import libraries and validate environment

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
```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,
});
```

### 5. Create a Tool Router session for Conveyor

What's happening:
- create spins up a short-lived MCP HTTP endpoint for this user
- The toolkits array contains "conveyor" for Conveyor access
- session.mcp.url is the MCP URL that Mastra's MCPClient will connect to
```typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["conveyor"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Conveyor MCP URL:", composioMCPUrl);
```

### 6. Configure Mastra MCP client and fetch tools

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 Conveyor toolkit
```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);
```

### 7. Create the Mastra agent

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

### 8. Set up interactive chat interface

What's happening:
- messages keeps the full conversation history in Mastra's expected format
- agent.generate runs the agent with conversation history and Conveyor 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
```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: {
        conveyor: 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);
});
```

## Complete Code

```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: ["conveyor"],
  });

  const composioMCPUrl = session.mcp.url;

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

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "conveyor-mastra-agent",
    instructions: "You are an AI agent with Conveyor 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: { conveyor: 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 Conveyor 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

## How to build Conveyor MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/conveyor/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/conveyor/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/conveyor/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/conveyor/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/conveyor/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/conveyor/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/conveyor/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/conveyor/framework/cli)
- [Google ADK](https://composio.dev/toolkits/conveyor/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/conveyor/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/conveyor/framework/ai-sdk)
- [LlamaIndex](https://composio.dev/toolkits/conveyor/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/conveyor/framework/crew-ai)

## Related Toolkits

- [Apilio](https://composio.dev/toolkits/apilio) - Apilio is a home automation platform that lets you connect and control smart devices from different brands. It helps you build flexible automations with complex conditions, schedules, and integrations.
- [Basin](https://composio.dev/toolkits/basin) - Basin is a no-code form backend for quickly setting up reliable contact forms. It lets you collect and manage form submissions without writing any server-side code.
- [Bouncer](https://composio.dev/toolkits/bouncer) - Bouncer is an email validation platform that verifies the authenticity of email addresses in real-time and batch. It helps boost deliverability and reduce bounce rates for your communications.
- [Crowdin](https://composio.dev/toolkits/crowdin) - Crowdin is a localization management platform that streamlines translation workflows and collaboration. It helps teams centralize multilingual content, boost productivity, and automate translation processes.
- [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights.
- [Detrack](https://composio.dev/toolkits/detrack) - Detrack is a delivery management platform for real-time tracking and proof of delivery. It helps businesses automate notifications and keep customers updated every step of the way.
- [Dnsfilter](https://composio.dev/toolkits/dnsfilter) - Dnsfilter is a cloud-based DNS security and content filtering solution. It helps organizations block online threats and manage safe internet access with ease.
- [Faraday](https://composio.dev/toolkits/faraday) - Faraday lets you embed AI in workflows across your stack for smarter automation. It boosts your favorite tools with actionable intelligence and seamless integration.
- [Feathery](https://composio.dev/toolkits/feathery) - Feathery is an AI-powered platform for building dynamic data intake forms with advanced logic. It helps teams automate complex workflows and collect structured data with ease.
- [Fillout forms](https://composio.dev/toolkits/fillout_forms) - Fillout forms is an online platform for building and managing forms with a flexible API. It lets you create, distribute, and collect responses from forms with ease.
- [Formdesk](https://composio.dev/toolkits/formdesk) - Formdesk is an online form builder for creating and managing professional forms. It's perfect for collecting data, automating workflows, and integrating form submissions with your favorite services.
- [Formsite](https://composio.dev/toolkits/formsite) - Formsite lets you build online forms and surveys with drag-and-drop simplicity. Capture, manage, and integrate form responses securely for streamlined workflows.
- [Graphhopper](https://composio.dev/toolkits/graphhopper) - GraphHopper is an enterprise-grade Directions API for routing, optimization, and geocoding across multiple vehicle types. It enables fast, reliable route planning and logistics automation for businesses.
- [Hyperbrowser](https://composio.dev/toolkits/hyperbrowser) - Hyperbrowser is a next-generation platform for scalable browser automation. It empowers AI agents to interact with web apps, automate workflows, and handle browser sessions at scale.
- [La Growth Machine](https://composio.dev/toolkits/lagrowthmachine) - La Growth Machine automates multi-channel sales outreach and routine tasks for sales teams. Streamline your workflow and focus on closing more deals.
- [Leverly](https://composio.dev/toolkits/leverly) - Leverly is a workflow automation platform that connects and coordinates actions across your apps. It streamlines repetitive processes so your business runs smoother, faster, and with fewer manual steps.
- [Maintainx](https://composio.dev/toolkits/maintainx) - Maintainx is a cloud-based CMMS for centralizing maintenance data, communication, and workflows. It helps organizations streamline maintenance operations and improve team coordination.
- [Make](https://composio.dev/toolkits/make) - Make is an automation platform that connects your favorite apps and services. Build powerful, custom workflows without writing code.
- [Ntfy](https://composio.dev/toolkits/ntfy) - Ntfy is a notification service to send push messages to phones or desktops. Instantly deliver alerts and updates to users, devices, or teams.
- [Persona](https://composio.dev/toolkits/persona) - Persona offers identity infrastructure to automate user verification and compliance. It helps organizations securely verify users and reduce fraud risk.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Conveyor MCP?

With a standalone Conveyor MCP server, the agents and LLMs can only access a fixed set of Conveyor tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Conveyor 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 Conveyor tools.

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

Yes, absolutely. You can configure which Conveyor 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 Conveyor data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)