How to integrate Nutrient Document Web Services API MCP with Vercel AI SDK v6

This guide walks you through connecting Nutrient Document Web Services API to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Nutrient Document Web Services API agent that can build documents, convert web pages to pdfs, ocr scanned pdfs, stage redactions, and add watermarks through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Nutrient Document Web Services API account through Composio's Nutrient Document Web Services API MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Nutrient Document Web Services API is a cloud document processing service for converting, OCRing, redacting, signing, and analyzing files. Use it to automate complex PDF and document workflows without running document infrastructure yourself.

11 Tools

Introduction

This guide walks you through connecting Nutrient Document Web Services API to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Nutrient Document Web Services API agent that can build documents, convert web pages to pdfs, ocr scanned pdfs, stage redactions, and add watermarks through natural language commands.

This guide will help you understand how to give your Vercel AI SDK agent real control over a Nutrient Document Web Services API account through Composio's Nutrient Document Web Services API MCP server.

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

Also integrate Nutrient Document Web Services API with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up and configure a Vercel AI SDK agent with Nutrient Document Web Services API integration
  • Using Composio's Tool Router to dynamically load and access Nutrient Document Web Services API 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 Nutrient Document Web Services API MCP server, and what's possible with it?

The Nutrient Document Web Services API MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Nutrient Document Web Services API account. It provides structured and secure access to document processing and account usage, so your agent can create and convert documents, OCR and improve PDFs, stage redactions, add watermarks, and check costs and usage on your behalf.

  • Document creation and conversion: Have your agent build documents from remote files, HTML, and new pages, or convert Markdown and public web pages into PDFs.
  • Searchable and accessible PDFs: Let the agent apply OCR to scanned PDFs or add screen-reader tags for PDF/UA accessibility.
  • Redaction review: Direct your agent to find matching content in a remote PDF and prepare a review copy with unapplied redaction marks.
  • PDF watermarking: Instruct your agent to add positioned text or image watermarks to every page of a PDF.
  • Cost and usage checks: Have your agent estimate a document job's required credits before processing, then review subscription status, remaining credits, and Processor usage.

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 you begin, make sure you have:
  • Node.js and npm installed
  • A Composio account with API key
  • An OpenAI API key
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install required dependencies

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

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
4

Set up environment variables

bash
OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here

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
5

Import required modules and validate environment

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,
});
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
6

Create Tool Router session and initialize MCP client

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

  const mcpUrl = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Nutrient Document Web Services API 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 Nutrient Document Web Services API-related tools through the MCP protocol
7

Connect to MCP server and retrieve tools

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();
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 Nutrient Document Web Services API tools that the agent can use
8

Initialize conversation and CLI interface

typescript
let messages: ModelMessage[] = [];

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

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

rl.prompt();
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
9

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

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);
});
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 Nutrient Document Web Services API 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

Complete Code

Here's the complete code to get you started with Nutrient Document Web Services API and Vercel AI SDK:

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: ["nutrient_document_web_services_api"],
  });

  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 nutrient_document_web_services_api, 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 Nutrient Document Web Services API 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.
TOOLS

Supported Tools

Every Nutrient Document Web Services API action and event your agent gets out of the box.

Analyze Build Cost

Estimate the credits and Processor features required by Build API instructions without executing the document job or consuming credits.

Build Document

Synchronously create one document from remote files, remote HTML, new pages, and composable safe actions, returning its filename, media type, and Base64 content up to 4 MB.

Convert Markdown to PDF

Convert CommonMark or GitHub-Flavored Markdown text into a PDF using optional built-in styling, custom CSS, page layout, and syntax highlighting settings.

Create PDF from Web Page

Render an HTML web page at a public URL, with optional page layout settings, into a PDF.

Get Account Information

Return the connected Nutrient account's subscription type, status, and estimated remaining credits.

Get Processor Usage

Return the connected account's Processor subscription status and credit usage counters.

Make PDF Accessible

Auto-tag a PDF at a remote URL for PDF/UA screen-reader accessibility and return the result's filename, media type, and Base64 content up to 4 MB.

OCR PDF

Apply optical character recognition to a PDF at a remote URL and return a searchable PDF's filename, media type, and Base64 content up to 4 MB.

Revoke JWT Token

Immediately revoke a bearer token created by CREATE_JWT_TOKEN using its ID.

Stage PDF Redactions

Find matching content in a remote PDF and return a review PDF's filename, media type, and Base64 content up to 4 MB with unapplied redaction marks.

Watermark PDF

Add a positioned text or image watermark to every page of a PDF at a public URL and return the copy's filename, media type, and Base64 content up to 4 MB.

FRAMEWORKS

How to build Nutrient Document Web Services API MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Nutrient Document Web Services API MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Nutrient Document Web Services API MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Nutrient Document Web Services API MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Nutrient Document Web Services API MCP with Claude Cowork

Codex logo

Codex

Use Nutrient Document Web Services API MCP with Codex

OpenClaw logo

OpenClaw

Use Nutrient Document Web Services API MCP with OpenClaw

Hermes logo

Hermes

Use Nutrient Document Web Services API MCP with Hermes

CLI logo

CLI

Use Nutrient Document Web Services API MCP with CLI

Google ADK logo

Google ADK

Use Nutrient Document Web Services API MCP with Google ADK

LangChain logo

LangChain

Use Nutrient Document Web Services API MCP with LangChain

Mastra AI logo

Mastra AI

Use Nutrient Document Web Services API MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Nutrient Document Web Services API MCP with LlamaIndex

CrewAI logo

CrewAI

Use Nutrient Document Web Services API MCP with CrewAI

FAQ

Frequently asked questions

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

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 Nutrient Document Web Services API tools.

Yes, absolutely. You can configure which Nutrient Document Web Services API 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 Nutrient Document Web Services API data and credentials are handled as safely as possible.

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