Affinda with Vercel AI SDK

How to integrate Affinda MCP with Vercel AI SDK v6

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Introduction Also integrate Affinda with TL;DR What is Vercel AI SDK What is the Affinda MCP server Supported Tools & Triggers Creating MCP Server - Stand-alone vs Composio SDK Step-by-step Guide Complete Code Conclusion How to build Affinda MCP Agent with another framework Explore Other Toolkits FAQ

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Introduction

This guide walks you through connecting Affinda to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Affinda agent that can extract invoice data from uploaded pdf, delete a document no longer needed, create a new tag for hr documents through natural language commands.

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

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

Also integrate Affinda with

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

TL;DR

Here's what you'll learn:

How to set up and configure a Vercel AI SDK agent with Affinda integration
Using Composio's Tool Router to dynamically load and access Affinda 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 Affinda MCP server, and what's possible with it?

The Affinda MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Affinda account. It provides structured and secure access to your document processing workflows, so your agent can upload files, extract data, organize workspaces, label documents, and automate annotation management on your behalf.

AI-powered document upload and extraction: Instantly have your agent upload new documents for parsing and extract structured data from various formats using Affinda's advanced AI models.
Workspace and collection management: Let your agent create, group, and organize documents into collections and workspaces, keeping your document processing streamlined and organized.
Automated annotation updates: Empower your agent to batch update or modify multiple document annotations in a single request, saving you time on manual corrections.
Document tagging and organization: Direct your agent to create tags and label documents, making it easy to categorize and quickly retrieve important files.
Effortless cleanup and resource management: Have your agent delete unwanted documents or collections, ensuring your Affinda account stays tidy and relevant at all times.

Supported Tools & Triggers

Tools

Add Tag to DocumentsTool to add a tag to multiple documents in a single operation.

Batch Update AnnotationsBatch update multiple document annotations in a single API call.

Create API UserTool to create a new API user within an organization.

Batch Create AnnotationsBatch create multiple document annotations in a single API call.

Create CollectionTool to create a new collection.

Create Data Field For CollectionTool to create a data field for a collection along with a new data point.

Create Data SourceTool to create a custom mapping data source.

Create Data Source ValueTool to add a new value to a mapping data source.

Create DocumentUpload a document to Affinda for parsing and data extraction.

Create Document TypeTool to create a new document type in the specified organization.

Create ExtractorTool to create a new extractor.

Create Document from DataCreate a document from structured resume or job description data for Search & Match.

Create IndexTool to create a new index for search and match functionality.

Create InvitationTool to create a new organization invitation.

Create Job Description SearchSearch through parsed job descriptions using custom criteria or resume matching.

Create Job Description Search Embed URLTool to create and return a signed URL for the embeddable job description search tool.

Create OrganizationTool to create a new organization.

Create RESTHook SubscriptionTool to create a new RESTHook subscription.

Create Resume SearchTool to search through parsed resumes using three methods: match to a job description, match to a resume, or custom criteria.

Create Resume Search Embed URLTool to create and return a signed URL for the embeddable resume search tool.

Create TagCreates a new tag in the specified workspace.

Create Validation ResultCreate a validation result for document annotations in Affinda.

Batch Create Validation ResultsBatch create multiple validation results for document annotations in a single API call.

Create WorkspaceTool to create a new workspace.

Create Workspace MembershipTool to add a user to a workspace by creating a membership.

Batch Delete AnnotationsBatch delete multiple document annotations in a single API call.

Delete CollectionPermanently delete a collection from Affinda by its identifier.

Delete Data SourcePermanently delete a mapping data source from the database by its identifier.

Delete Data Source ValueTool to delete a specific value from a mapping data source.

Delete DocumentTool to delete a specific document by its ID.

Delete Document TypeTool to permanently delete a document type by its identifier.

Delete IndexTool to permanently delete an index from Affinda by its name.

Delete InvitationTool to delete an invitation by its identifier.

Delete OrganizationPermanently deletes an organization from Affinda.

Delete Resthook SubscriptionTool to delete a specific resthook subscription by ID.

Delete TagPermanently delete a tag from Affinda by its ID.

Delete Validation ResultsDelete multiple validation results in a single API call.

Delete WorkspaceTool to delete a specific workspace by its ID.

Delete Workspace MembershipTool to remove a user from a workspace by membership ID.

Get All API UsersTool to retrieve a list of all API users.

Get All Document SplittersTool to get a list of all document splitters.

Get All InvitationsTool to retrieve all invitations you created or sent to you.

Get Organization MembershipsRetrieve all organization memberships across the account.

Get TagsTool to list all tags.

Get All Validation ResultsTool to list validation results for documents.

Get Workspace MembershipsRetrieve all workspace memberships across the account.

Get AnnotationsRetrieves all annotations for a specific document.

Get CollectionTool to retrieve details of a specific collection by its ID.

Get CollectionsTool to retrieve a list of all collections.

Get Data SourceTool to retrieve details of a specific mapping data source by its identifier.

Get Data Source ValueTool to retrieve a specific value from a mapping data source.

Get Data Source ValuesTool to retrieve all values from a mapping data source.

Get DocumentRetrieve full details and parsed data for a specific document by its identifier.

Get Document RedactedTool to retrieve the redacted version of a document as a PDF file.

Get DocumentsTool to retrieve a list of all documents.

Get Document SplitterTool to retrieve details of a specific document splitter by its identifier.

Get Document TypeTool to retrieve details of a specific document type by its ID.

Get Document Type JSON SchemaTool to generate a JSON schema from a document type by its identifier.

Get Document Type Pydantic ModelsTool to generate Pydantic model code from a document type's schema.

Get Document TypesRetrieve all document types accessible to the authenticated user.

Get ExtractorTool to retrieve detailed information about a specific extractor by its identifier.

Get ExtractorsRetrieve all extractors available for an organization.

Get Index DocumentsTool to retrieve all indexed documents for a specific index.

Get InvitationTool to retrieve details of a specific organization invitation by its identifier.

Get Job Description Search ConfigTool to get the configuration for the logged in user's embeddable job description search tool.

Get MappingTool to retrieve a specific mapping by its identifier.

Get OrganizationTool to retrieve details of a specific organization by its ID.

Get Organization MembershipTool to retrieve details of a specific organization membership by its ID.

Get OrganizationsRetrieves all organizations accessible to the authenticated user.

Get Resthook SubscriptionTool to retrieve details of a specific resthook subscription by its ID.

Get RESTHook SubscriptionsTool to retrieve a list of all RESTHook subscriptions.

Get TagTool to retrieve details of a specific tag by its ID.

Get Usage by WorkspaceRetrieves monthly document processing usage statistics for a specific workspace.

Get WorkspaceTool to retrieve details of a specific workspace by its ID.

Get Workspace MembershipTool to retrieve details of a specific workspace membership by its ID.

Get WorkspacesTool to retrieve a list of all workspaces.

List Data PointsTool to retrieve all data points.

List Data SourcesTool to retrieve the list of all custom mapping data sources.

List IndexesTool to retrieve a list of all search indexes.

List MappingsTool to retrieve the list of all custom data mappings.

List Occupation GroupsTool to retrieve the list of searchable occupation groups.

List Resume Search ConfigTool to get the configuration for the logged in user's embeddable resume search tool.

List Resume Search Job Title SuggestionsTool to get job title suggestions based on provided job title(s).

List Resume Search Skill SuggestionsTool to get skill suggestions based on provided skills.

Remove Tag from DocumentsRemove a tag from multiple documents in a single batch operation.

Replace Data Source ValuesTool to completely replace all values in a mapping data source.

Split Document PagesSplit a document into multiple documents by dividing its pages.

Update AnnotationTool to update data of a single annotation in Affinda.

Update CollectionTool to update specific fields of a collection.

Update Data Field For CollectionTool to update a data field configuration for a collection's data point.

Update Data Source ValueTool to update an existing value in a mapping data source.

Update DocumentTool to update specific fields of a document.

Update Document DataUpdate parsed data for a resume or job description document in Affinda.

Update Document TypeTool to update a document type by its identifier.

Update ExtractorTool to update specific fields of an extractor.

Update IndexTool to update the name of an existing search index.

Update InvitationTool to update an organization invitation's role.

Update Job Description Search ConfigTool to update the configuration for the logged in user's embeddable job description search tool.

Update MappingTool to update a specific mapping's settings.

Update OrganizationTool to update specific fields of an organization.

Update Organization MembershipTool to update an organization membership's role.

Update RESTHook SubscriptionTool to update an existing RESTHook subscription.

Update Resume Search ConfigTool to update the configuration for the logged in user's embeddable resume search tool.

Update TagTool to update data of a tag.

Update WorkspaceTool to update specific fields of a workspace.

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

Prerequisites

Before you begin, make sure you have:

Node.js and npm installed
A Composio account with API key
An OpenAI API key

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.

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

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

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

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: ["affinda"],
  });

  const mcpUrl = session.mcp.url;

What's happening:

We're creating a Tool Router session that gives your agent access to Affinda 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 Affinda-related tools through the MCP protocol

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 Affinda tools that the agent can use

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 affinda, 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

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 Affinda 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 Affinda 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: ["affinda"],
  });

  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 affinda, 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 Affinda 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.

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TOOLKIT MARKETPLACE

FAQ

What are the differences in Tool Router MCP and Affinda MCP?

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

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

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

Used by agents from

Never worry about agent reliability

We handle tool reliability, observability, and security so you never have to second-guess an agent action.

Get started for free Get a demo↗

Harsha GaddipatiCo-founder, Slashy

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

Abhi AryaCo-founder, Opennote

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 has immensely helped in keeping new users on the platform.

Nirman DaveCEO, Zams

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

Ryan YuFounder, Extra Thursday

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

Tomisin JenrolaFounder & CEO, SwarmZero

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

Jerome LeclancheCo-Founder, Ingram Technologies

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.

Harsha GaddipatiCo-founder, Slashy

Abhi AryaCo-founder, Opennote

Nirman DaveCEO, Zams

Ryan YuFounder, Extra Thursday

Tomisin JenrolaFounder & CEO, SwarmZero

Jerome LeclancheCo-Founder, Ingram Technologies

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.

Harsha GaddipatiCo-founder, Slashy

Abhi AryaCo-founder, Opennote

Nirman DaveCEO, Zams

Ryan YuFounder, Extra Thursday

Tomisin JenrolaFounder & CEO, SwarmZero

Jerome LeclancheCo-Founder, Ingram Technologies

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.

How to integrate Affinda MCP with Vercel AI SDK v6

Table of Contents

Connect Affinda without Auth hassles

Introduction

Also integrate Affinda with

TL;DR

What is Vercel AI SDK?

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

Supported Tools & Triggers

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 required dependencies

Set up environment variables

Import required modules and validate environment

Create Tool Router session and initialize MCP client

Connect to MCP server and retrieve tools

Initialize conversation and CLI interface

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

Complete Code

Conclusion

How to build Affinda MCP Agent with another framework

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Claude Agent SDK

Claude Code

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Codex

OpenClaw

Hermes

CLI

Google ADK

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Mastra AI

LlamaIndex

CrewAI

Explore Other Toolkits

Google Drive

Google Docs

Google Super

FAQ

What are the differences in Tool Router MCP and Affinda MCP?

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

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

How safe is my data with Composio Tool Router?

Used by agents from

Never worry about agent reliability