How to integrate Cradl AI MCP with Mastra AI

This guide walks you through connecting Cradl AI to Mastra AI using the Composio tool router. By the end, you'll have a working Cradl AI agent that can extract invoice totals from uploaded pdfs, list failed cradl ai extraction jobs, review purchase order extraction results through natural language commands. This guide will help you understand how to give your Mastra AI agent real control over a Cradl AI account through Composio's Cradl AI MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Cradl AI

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Cradl AI is an API-first platform for AI-powered document extraction workflows. Use it to turn invoices, forms, and PDFs into structured data at scale.

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Introduction

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

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

Also integrate Cradl AI with

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK LlamaIndex CrewAI

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

The Cradl AI MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Cradl AI account. It provides structured and secure access to your document extraction workflows, so your agent can upload and process documents, start and monitor processing runs, review extracted predictions, and download results on your behalf.

Document processing: Have your agent submit a PDF, image, or email to a selected document processing agent for asynchronous extraction.
Agent and document discovery: Let the agent list available document processing agents and browse documents in your connected workspace.
Processing run management: Direct your agent to start processing runs, list past runs, filter them by status or time, and check their current state and outputs.
Prediction review: Instruct your agent to list extracted predictions and inspect their status, extracted fields, errors, and output references.
File retrieval: Have your agent download document content, OCR results, annotations, predictions, or processing variables returned by Cradl AI.

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:

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

Step-by-step Guide

Step by step09 STEPS

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 Cradl AI 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 Cradl AI

typescript

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

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

What's happening:

create spins up a short-lived MCP HTTP endpoint for this user
The toolkits array contains "cradl_ai" for Cradl AI 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 Cradl AI toolkit

Create the Mastra agent

typescript

const agent = new Agent({
    name: "cradl_ai-mastra-agent",
    instructions: "You are an AI agent with Cradl AI 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: {
        cradl_ai: 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 Cradl AI 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 Cradl AI 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: ["cradl_ai"],
  });

  const composioMCPUrl = session.mcp.url;

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

  const composioTools = await mcpClient.getTools();

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

TOOLS

Supported Tools

Every Cradl AI action and event your agent gets out of the box.

Download File

Download document, OCR, annotation, prediction, or run-variable content from a Cradl AI file URL returned by another Cradl AI tool.

Get Agent Run

Get the current state and outputs of one Cradl AI agent run.

Get Document

Get metadata, processing links, and annotations for one Cradl AI document by ID.

Get Prediction

Get one extracted prediction, including its status, fields, errors, and downloadable output references.

List Agent Runs

List processing runs for an agent, optionally filtered by status or creation/update time.

List Agents

List document-processing agents available to the connected Cradl AI workspace.

List Documents

List documents in the connected Cradl AI workspace and discover document IDs and content/output file URLs.

List Predictions

List extracted predictions, optionally filtered by model, and discover prediction IDs and output file URLs.

Process Document

Create a persistent Cradl agent run and document, then upload one document for asynchronous extraction in a single call.

Start Agent Run

Create a processing run for an existing Cradl AI agent.

Upload Document to Run

Create a document attached to a Cradl AI agent run and upload its PDF, image, or email bytes.

FRAMEWORKS

How to build Cradl AI MCP Agent with another framework

OpenAI Agents SDK

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Claude Agent SDK

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Claude Code

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Claude Cowork

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Codex

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OpenClaw

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Hermes

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CLI

Use Cradl AI MCP with CLI

Google ADK

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LangChain

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Vercel AI SDK

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LlamaIndex

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CrewAI

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Castingwords

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FAQ

Frequently asked questions

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

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 Cradl AI tools.

Yes, absolutely. You can configure which Cradl AI 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.

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 Cradl AI data and credentials are handled as safely as possible.

Start with Cradl AI.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Cradl AI tool your agent needs.Free to start.

Start building

Customer stories

Teams ship real agents on Composio

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Cradl AI MCP with Mastra AI

Connect Cradl AI without auth hassles

Introduction

Also integrate Cradl AI with

TL;DR

What is Mastra AI?

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

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import libraries and validate environment

Create a Tool Router session for Cradl AI

Configure Mastra MCP client and fetch tools

Create the Mastra agent

Set up interactive chat interface

Complete Code

Conclusion

Supported Tools

How to build Cradl AI MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

LlamaIndex

CrewAI

Explore Other Toolkits

Boloforms

Box

Carbone

Castingwords

Frequently asked questions

What are the differences in Tool Router MCP and Cradl AI MCP?+

Can I use Tool Router MCP with Mastra AI?+

Can I manage the permissions and scopes for Cradl AI while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with Cradl AI.It takes 30 seconds.

Teams ship real agents on Composio