How to integrate CrowTerminal MCP with Mastra AI

This guide walks you through connecting CrowTerminal to Mastra AI using the Composio tool router. By the end, you'll have a working CrowTerminal agent that can debug failing docker build command, automate repeated git cleanup commands, recall yesterday's terminal troubleshooting notes through natural language commands. This guide will help you understand how to give your Mastra AI agent real control over a CrowTerminal account through Composio's CrowTerminal MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

CrowTerminal logoCrowTerminal
Api KeyBasic

CrowTerminal is an AI-powered command line assistant with memory and automation for developers. Use it to speed up terminal work, keep context, and automate repetitive dev tasks.

27 Tools

Introduction

This guide walks you through connecting CrowTerminal to Mastra AI using the Composio tool router. By the end, you'll have a working CrowTerminal agent that can debug failing docker build command, automate repeated git cleanup commands, recall yesterday's terminal troubleshooting notes through natural language commands.

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

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

Also integrate CrowTerminal 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 CrowTerminal tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch CrowTerminal 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 CrowTerminal 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 CrowTerminal MCP server, and what's possible with it?

The CrowTerminal MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your CrowTerminal account. It provides structured and secure access so your agent can perform CrowTerminal operations on your behalf.

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

Step by step09 STEPS
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
2

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 CrowTerminal through MCP.
3

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
4

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
5

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
6

Create a Tool Router session for CrowTerminal

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

  const composioMCPUrl = session.mcp.url;
  console.log("CrowTerminal MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "crowterminal" for CrowTerminal access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to
7

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 CrowTerminal toolkit
8

Create the Mastra agent

typescript
const agent = new Agent({
    name: "crowterminal-mastra-agent",
    instructions: "You are an AI agent with CrowTerminal 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
9

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: {
        crowterminal: 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 CrowTerminal 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 CrowTerminal 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: ["crowterminal"],
  });

  const composioMCPUrl = session.mcp.url;

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

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "crowterminal-mastra-agent",
    instructions: "You are an AI agent with CrowTerminal 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: { crowterminal: 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 CrowTerminal 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 CrowTerminal action and event your agent gets out of the box.

Analyze Agent Engagement

Tool to analyze engagement correlation for every field in your agent's markdown.

Compare Agent Markdown

Tool to compare your agent's markdown directly with all stored versions.

Create Webhook

Tool to register a new webhook for receiving real-time event notifications from CrowTerminal.

Delete Webhook

Tool to delete an existing webhook registration.

Get BYOK Platform Intelligence

Tool to get algorithm insights for TikTok, Instagram, and YouTube without client-specific context.

Get Client Memory Changelog

Retrieve human-readable change history for a client's memory.

Get Client Memory Pattern

Tool to track a specific field over time for trend analysis.

Get Components Status

Tool to get detailed status of each CrowTerminal service component.

Get Data Types

Tool to retrieve valid data types for ingestion across platforms.

Get Recent Incidents

Tool to retrieve list of recent incidents from CrowTerminal with duration and affected components.

Get Platform Intelligence

Tool to retrieve algorithm insights for TikTok, Instagram, and YouTube.

Get Sandbox Client

Tool to get mock client data for testing in the sandbox environment.

Get Sandbox Memory

Tool to retrieve mock memory/skill data for testing purposes.

Get Service Status

Retrieve CrowTerminal service status including overall health, component metrics, and uptime data.

Get Status History

Tool to get 7-day uptime data points ready for visualization and charting.

Get Uptime Data

Tool to retrieve historical uptime data for CrowTerminal agents.

Bulk Ingest Analytics Data

Tool to bulk ingest up to 50 analytics data points at once to CrowTerminal.

Ingest Analytics Data

Tool to ingest platform analytics data from TikTok Studio, Instagram Insights, or YouTube Analytics.

List Webhooks

Tool to list all registered webhooks for the authenticated agent.

Ping CrowTerminal Service

Tool to check CrowTerminal service availability via a simple ping endpoint.

Bulk Read Memory

Tool to read memory for multiple clients at once (up to 50).

Register Agent

Tool to self-register a new agent and obtain an API key.

Sandbox Engagement Analysis

Tool to run mock engagement analysis in the CrowTerminal sandbox environment.

Test Webhook

Tool to test a webhook URL by sending a test payload.

Update Webhook

Tool to update an existing webhook configuration in CrowTerminal.

Validate Proposed Changes

Tool to validate proposed changes against historical data before updating memory.

Validate Sandbox

Tool to mock validation endpoint for testing in sandbox.

FAQ

Frequently asked questions

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

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

Start with CrowTerminal.It takes 30 seconds.

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

Start building