# How to integrate Mx toolbox MCP with Vercel AI SDK v6

```json
{
  "title": "How to integrate Mx toolbox MCP with Vercel AI SDK v6",
  "toolkit": "Mx toolbox",
  "toolkit_slug": "mx_toolbox",
  "framework": "Vercel AI SDK",
  "framework_slug": "ai-sdk",
  "url": "https://composio.dev/toolkits/mx_toolbox/framework/ai-sdk",
  "markdown_url": "https://composio.dev/toolkits/mx_toolbox/framework/ai-sdk.md",
  "updated_at": "2026-05-12T10:19:42.473Z"
}
```

## Introduction

This guide walks you through connecting Mx toolbox to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Mx toolbox agent that can check if your domain is blacklisted, get current mx records for example.com, run a ping test on our mail server through natural language commands.
This guide will help you understand how to give your Vercel AI SDK agent real control over a Mx toolbox account through Composio's Mx toolbox MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Mx toolbox with

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

## TL;DR

Here's what you'll learn:
- How to set up and configure a Vercel AI SDK agent with Mx toolbox integration
- Using Composio's Tool Router to dynamically load and access Mx toolbox tools
- Creating an MCP client connection using HTTP transport
- Building an interactive CLI chat interface with conversation history management
- Handling tool calls and results within the Vercel AI SDK framework

## What is Vercel AI SDK?

The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state.
Key features include:
- streamText: Core function for streaming responses with real-time tool support
- MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp
- Step Counting: Control multi-step tool execution with stopWhen: stepCountIs()
- OpenAI Provider: Native integration with OpenAI models

## What is the Mx toolbox MCP server, and what's possible with it?

The Mx toolbox MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Mx toolbox account. It provides structured and secure access to network diagnostic and email health tools, so your agent can perform actions like DNS lookups, blacklist checks, email authentication analysis, and connectivity testing on your behalf.
- Automated DNS and MX record lookups: Instantly retrieve DNS, MX, DKIM, DMARC, and MTA-STS records for any domain to verify configuration and troubleshoot email delivery issues.
- Blacklist monitoring and alerting: Check if your domain or IP is listed on common blacklists, helping you stay ahead of email deliverability problems and security risks.
- Email authentication validation: Validate BIMI, DKIM, and DMARC records to ensure your domain's outgoing emails are properly authenticated and protected against spoofing.
- Network and SMTP diagnostics: Run ping, HTTP, and SMTP lookups to diagnose connectivity issues, measure latency, or assess mail server responsiveness—no manual testing required.
- Brand and security checks: Use BIMI and MTA-STS lookups to confirm your brand indicators and mail transport security policies are correctly published and compliant.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `MX_TOOLBOX_LOOKUP_BIMI_RECORD` | Lookup BIMI Record | Retrieves BIMI (Brand Indicators for Message Identification) record and diagnostic information for a domain. BIMI allows organizations to display verified logos in email clients. This tool checks: - Whether a BIMI record exists at default._bimi.{domain} - DMARC policy requirements (quarantine or reject needed for BIMI) - DNS resolution details and nameserver information - Related diagnostic checks and recommendations Returns comprehensive lookup data including passed/failed checks, DMARC records, DNS transcript, and related lookups. Use when verifying email branding configuration or troubleshooting BIMI implementation. |
| `MX_TOOLBOX_LOOKUP_BLACKLIST` | Lookup Blacklist | Tool to perform a blacklist check on a domain or IP. Use when you need to verify whether a domain or IP is listed in common blacklists. |
| `MX_TOOLBOX_LOOKUP_DKIM` | Lookup DKIM Record | Tool to retrieve DKIM (DomainKeys Identified Mail) records for a domain. DKIM is an email authentication method that helps prevent email spoofing by allowing the receiver to verify that an email was actually sent and authorized by the owner of that domain. Use this tool to verify DKIM configuration and troubleshoot email authentication issues. |
| `MX_TOOLBOX_LOOKUP_DMARC` | Lookup DMARC Record | Retrieves DMARC (Domain-based Message Authentication, Reporting & Conformance) records for a domain and performs validation checks. Returns detailed information about the DMARC record including policy settings (reject/quarantine/none), reporting addresses, alignment modes, and diagnostic check results. Useful for verifying email authentication configuration and troubleshooting email delivery issues. |
| `MX_TOOLBOX_LOOKUP_DNS` | Lookup DNS Records | Performs comprehensive DNS health check and retrieves name server records for a domain. Returns detailed diagnostics including: - Name server (NS) records with IP addresses, TTL, and status - DNS configuration health checks (warnings, errors, passed tests) - Query transcripts showing DNS resolution path - Related lookup suggestions (A, MX, SPF records) Use this to diagnose DNS issues, verify name server configurations, or check DNS propagation status. |
| `MX_TOOLBOX_LOOKUP_HTTP` | HTTP Lookup | Tool to perform an HTTP test on a domain. Use when you need to assess HTTP connectivity and status for a given domain. |
| `MX_TOOLBOX_LOOKUP_MTA_STS_RECORD` | Lookup MTA-STS Record | Tool to lookup MTA-STS record for a domain. Use when validating mail transport security policy. |
| `MX_TOOLBOX_LOOKUP_MX` | Lookup MX Records | Retrieves MX (Mail Exchange) records for a domain. Returns the mail servers responsible for receiving email for the domain, including their priority, hostname, IP address, and TTL. Use this to discover and verify email infrastructure for any domain. |
| `MX_TOOLBOX_LOOKUP_PING` | Ping Lookup | Performs a ping test to check network connectivity and measure round-trip time to a domain or IP address. Returns detailed ping statistics including response time, TTL (Time-To-Live), packet size, and ASN (Autonomous System Number) information. Useful for diagnosing network connectivity issues, measuring latency, and verifying host availability. |
| `MX_TOOLBOX_LOOKUP_SMTP` | SMTP Lookup | Tool to perform an SMTP connectivity test on a domain. Returns diagnostic results including connection status, DNS checks, TLS support, and other email deliverability indicators. Use when verifying SMTP server configuration or troubleshooting email delivery issues for a domain. |
| `MX_TOOLBOX_LOOKUP_SPF` | Lookup SPF Record | Tool to retrieve SPF records for a specified domain. Use when confirming email sender authorization policies. |
| `MX_TOOLBOX_MONITOR_STATUS` | Monitor Status | Retrieves the current status of all monitors configured in the MX Toolbox account. This action returns a list of all monitors with their health status, last check time, reputation scores, and any failing checks or warnings. Use this when you need to: - Check the overall health of all configured monitors - Get a comprehensive view of monitoring status across all services - Identify which monitors are failing or have warnings - Review when monitors were last checked No parameters are required - this action retrieves all monitors for the authenticated account. Returns an empty list if no monitors are configured. |
| `MX_TOOLBOX_USAGE_CHECK` | Check Usage | Retrieve API usage statistics for DNS and network lookups. Returns current request counts, maximum allowed requests, and any overage errors for both DNS lookups and network operations (HTTP, SMTP, Ping). |

## Supported Triggers

None listed.

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

The Mx toolbox MCP server is an implementation of the Model Context Protocol that connects your AI agent to Mx toolbox. It provides structured and secure access so your agent can perform Mx toolbox 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 you begin, make sure you have:
- Node.js and npm installed
- A Composio account with API key
- An OpenAI API key

### 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'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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install required dependencies

First, install the necessary packages for your project.
What you're installing:
- @ai-sdk/openai: Vercel AI SDK's OpenAI provider
- @ai-sdk/mcp: MCP client for Vercel AI SDK
- @composio/core: Composio SDK for tool integration
- ai: Core Vercel AI SDK
- dotenv: Environment variable management
```bash
npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv
```

### 3. Set up environment variables

Create a .env file in your project root.
What's needed:
- OPENAI_API_KEY: Your OpenAI API key for GPT model access
- COMPOSIO_API_KEY: Your Composio API key for tool access
- COMPOSIO_USER_ID: A unique identifier for the user session
```bash
OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
```

### 4. Import required modules and validate environment

What's happening:
- We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio
- The dotenv/config import automatically loads environment variables
- The MCP client import enables connection to Composio's tool server
```typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

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

if (!process.env.OPENAI_API_KEY) 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,
});
```

### 5. Create Tool Router session and initialize MCP client

What's happening:
- We're creating a Tool Router session that gives your agent access to Mx toolbox tools
- The create method takes the user ID and specifies which toolkits should be available
- The returned mcp object contains the URL and authentication headers needed to connect to the MCP server
- This session provides access to all Mx toolbox-related tools through the MCP protocol
```typescript
async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["mx_toolbox"],
  });

  const mcpUrl = session.mcp.url;
```

### 6. Connect to MCP server and retrieve tools

What's happening:
- We're creating an MCP client that connects to our Composio Tool Router session via HTTP
- The mcp.url provides the endpoint, and mcp.headers contains authentication credentials
- The type: "http" is important - Composio requires HTTP transport
- tools() retrieves all available Mx toolbox tools that the agent can use
```typescript
const mcpClient = await createMCPClient({
  transport: {
    type: "http",
    url: mcpUrl,
    headers: session.mcp.headers, // Authentication headers for the Composio MCP server
  },
});

const tools = await mcpClient.tools();
```

### 7. Initialize conversation and CLI interface

What's happening:
- We initialize an empty messages array to maintain conversation history
- A readline interface is created to accept user input from the command line
- Instructions are displayed to guide the user on how to interact with the agent
```typescript
let messages: ModelMessage[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log(
  "Ask any questions related to mx_toolbox, like summarize my last 5 emails, send an email, etc... :)))\n",
);

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

rl.prompt();
```

### 8. Handle user input and stream responses with real-time tool feedback

What's happening:
- We use streamText instead of generateText to stream responses in real-time
- toolChoice: "auto" allows the model to decide when to use Mx toolbox tools
- stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations
- onStepFinish callback displays which tools are being used in real-time
- We iterate through the text stream to create a typewriter effect as the agent responds
- The complete response is added to conversation history to maintain context
- Errors are caught and displayed with helpful retry suggestions
```typescript
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({ role: "user", content: trimmedInput });
  console.log("\nAgent is thinking...\n");

  try {
    const stream = streamText({
      model: openai("gpt-5"),
      messages,
      tools,
      toolChoice: "auto",
      stopWhen: stepCountIs(10),
      onStepFinish: (step) => {
        for (const toolCall of step.toolCalls) {
          console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    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 { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

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

if (!process.env.OPENAI_API_KEY) 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,
});

async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["mx_toolbox"],
  });

  const mcpUrl = session.mcp.url;

  const mcpClient = await createMCPClient({
    transport: {
      type: "http",
      url: mcpUrl,
      headers: session.mcp.headers, // Authentication headers for the Composio MCP server
    },
  });

  const tools = await mcpClient.tools();

  let messages: ModelMessage[] = [];

  console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
  console.log(
    "Ask any questions related to mx_toolbox, like summarize my last 5 emails, send an email, etc... :)))\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({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    try {
      const stream = streamText({
        model: openai("gpt-5"),
        messages,
        tools,
        toolChoice: "auto",
        stopWhen: stepCountIs(10),
        onStepFinish: (step) => {
          for (const toolCall of step.toolCalls) {
            console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

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

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
```

## Conclusion

You've successfully built a Mx toolbox agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback.
Key features of this implementation:
- Real-time streaming responses for a better user experience with typewriter effect
- Live tool execution feedback showing which tools are being used as the agent works
- Dynamic tool loading through Composio's Tool Router with secure authentication
- Multi-step tool execution with configurable step limits (up to 10 steps)
- Comprehensive error handling for robust agent execution
- Conversation history maintenance for context-aware responses
You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

## How to build Mx toolbox MCP Agent with another framework

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

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- [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place.
- [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences.
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- [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction.
- [Apiflash](https://composio.dev/toolkits/apiflash) - Apiflash is a website screenshot API for programmatically capturing web pages. It delivers high-quality screenshots on demand for automation, monitoring, or reporting.
- [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache.
- [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence.
- [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place.
- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
- [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure.
- [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code.
- [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows.
- [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights.
- [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews.
- [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease.
- [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Mx toolbox MCP?

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

### Can I use Tool Router MCP with Vercel AI SDK v6?

Yes, you can. Vercel AI SDK v6 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 Mx toolbox tools.

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

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

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