How to integrate VLM Run MCP with Claude Agent SDK

This guide walks you through connecting VLM Run to the Claude Agent SDK using the Composio tool router. By the end, you'll have a working VLM Run agent that can upload files, extract structured data, run agents, find skills, and review run artifacts through natural language commands. This guide will help you understand how to give your Claude Agent SDK agent real control over a VLM Run account through Composio's VLM Run MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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VLM Run is a multimodal AI platform for structured extraction, predictions, files, skills, feedback, and evaluations. It helps teams build reliable vision-language workflows without stitching together model, file, and eval infrastructure by hand.

11 Tools

Introduction

This guide walks you through connecting VLM Run to the Claude Agent SDK using the Composio tool router. By the end, you'll have a working VLM Run agent that can upload files, extract structured data, run agents, find skills, and review run artifacts through natural language commands.

This guide will help you understand how to give your Claude Agent SDK agent real control over a VLM Run account through Composio's VLM Run MCP server.

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

Also integrate VLM Run with

OpenAI Agents SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your Claude/Anthropic and Composio API keys
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for VLM Run
  • Configure an AI agent that can use VLM Run as a tool
  • Run a live chat session where you can ask the agent to perform VLM Run operations

What is Claude Agent SDK?

The Claude Agent SDK is Anthropic's official framework for building AI agents powered by Claude. It provides a streamlined interface for creating agents with MCP tool support and conversation management.

Key features include:

  • Native MCP Support: Built-in support for Model Context Protocol servers
  • Permission Modes: Control tool execution permissions
  • Streaming Responses: Real-time response streaming for interactive applications
  • Context Manager: Clean async context management for sessions

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

The VLM Run MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your VLM Run account. It provides structured and secure access to your multimodal agents, files, skills, extraction workflows, and runs, so your agent can upload files, extract structured data, execute agents, create reusable skills, and track results on your behalf.

  • Structured data extraction: Have your agent extract structured JSON from documents, images, videos, or audio using a supported domain, custom schema, or existing skill.
  • Multimodal agent execution: Direct your agent to choose an available VLM Run agent or provide an inline configuration, then run it against multimodal inputs.
  • File and skill management: Let the agent upload local files, find previously uploaded files, browse existing skills, or create a reusable skill from an uploaded zip, prompt, or chat session.
  • Run tracking and result retrieval: Instruct your agent to list extraction and agent runs, check work in progress, and retrieve completed results.
  • Schema and artifact discovery: Have your agent explore supported extraction domains and schemas, then list artifacts created during a chat session or agent execution.

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:
  • Composio API Key and Claude/Anthropic API Key
  • Primary know-how of Claude Agents SDK
  • A VLM Run account
  • Some knowledge of Python
2

Getting API Keys for Claude/Anthropic and Composio

Claude/Anthropic API Key
  • Go to the Anthropic Console and create an API key. You'll need credits to use the models.
  • 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 dependencies

npm install @anthropic-ai/claude-agent-sdk @composio/core dotenv

Install the Composio SDK and the Claude Agents SDK.

What's happening:

  • @composio/core provides Composio integration for Anthropic
  • @anthropic-ai/claude-agent-sdk is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
ANTHROPIC_API_KEY=your_anthropic_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID identifies the user for session management
  • ANTHROPIC_API_KEY authenticates with Anthropic/Claude
5

Import dependencies

import 'dotenv/config';
import readline from 'node:readline';
import { Composio } from '@composio/core';
import { query, type Options } from "@anthropic-ai/claude-agent-sdk";

dotenv.config();
What's happening:
  • We're importing all necessary libraries including the Claude Agent SDK and Composio
  • The dotenv.config() function loads environment variables from your .env file
  • This setup prepares the foundation for connecting Claude with VLM Run functionality
6

Create a Composio instance and Tool Router session

async function chat() {
  const { COMPOSIO_API_KEY, USER_ID } = process.env;
  if (!COMPOSIO_API_KEY || !USER_ID) {
    throw new Error('COMPOSIO_API_KEY and USER_ID required in .env');
  }

  const composio = new Composio({ apiKey: COMPOSIO_API_KEY });

  // Create Tool Router session for VLM Run
  const session = await composio.create(USER_ID, {
    toolkits: ['vlm_run'],
  });
  const mcpUrl = session?.mcp.url;
What's happening:
  • The function checks for the required COMPOSIO_API_KEY environment variable
  • We're creating a Composio instance using our API key
  • The create method creates a Tool Router session for VLM Run
  • The returned url is the MCP server URL that your agent will use
7

Configure Claude Agent with MCP

const options: Options = {
  permissionMode: 'bypassPermissions',
  mcpServers: {
    composio: {
      type: 'http',
      url: mcpUrl,
      headers: { 'x-api-key': COMPOSIO_API_KEY }
    }
  },
  systemPrompt: 'You are a helpful assistant with access to VLM Run tools via Composio.',
  maxTurns: 10,
};
What's happening:
  • We're configuring the Claude Agent options with the MCP server URL
  • permissionMode: 'bypassPermissions' allows the agent to execute operations without asking for permission each time
  • The system prompt instructs the agent that it has access to VLM Run
  • maxTurns: 10 limits the conversation length to prevent excessive API usage
8

Create client and start chat loop

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

  console.log('\nChat started. Type "exit" to quit.\n');

  let isProcessing = false;

  async function ask(prompt: string) {
    isProcessing = true;
    rl.pause();

    process.stdout.write('Claude is thinking...');
    const stream = query({ prompt, options });

    let firstChunk = true;
    for await (const msg of stream) {
      const content = (msg as any).message?.content || (msg as any).content;
      if (Array.isArray(content)) {
        for (const block of content) {
          if (block.type === 'text' && block.text) {
            if (firstChunk) {
              process.stdout.write('\r\x1b[K');
              process.stdout.write('Claude: ');
              firstChunk = false;
            }
            process.stdout.write(block.text);
          }
        }
      }
    }
    process.stdout.write('\n\n');

    isProcessing = false;
    rl.resume();
    rl.prompt();
  }

  rl.on('line', async (line) => {
    if (isProcessing) return;

    const input = line.trim();
    if (input === 'exit') {
      rl.close();
      process.exit(0);
    }
    if (input) await ask(input);
    else rl.prompt();
  });

  await ask('What can you help me with?');
}
What's happening:
  • The readline interface is created to handle user input and output
  • The query function is used to send the user's input to the agent
  • The chat loop continues until the user types 'exit' or 'quit'
9

Run the application

try {
  await chat();
} catch (error) {
  console.error(error);
  process.exit(1);
}
What's happening:
  • The chat function is the entry point for the application
  • The try-catch block is used to handle any errors that occur

Complete Code

Here's the complete code to get you started with VLM Run and Claude Agent SDK:

import 'dotenv/config';
import readline from 'node:readline';
import { Composio } from '@composio/core';
import { query, type Options } from "@anthropic-ai/claude-agent-sdk";

async function chat() {
  const { COMPOSIO_API_KEY, USER_ID } = process.env;
  if (!COMPOSIO_API_KEY || !USER_ID) {
    throw new Error('COMPOSIO_API_KEY and USER_ID required in .env');
  }

  const composio = new Composio({ apiKey: COMPOSIO_API_KEY });
  const session = await composio.create(USER_ID, {
    toolkits: ['vlm_run']
  });
  const mcp_url = session?.mcp.url;

  const options: Options = {
    permissionMode: 'bypassPermissions',
    mcpServers: {
      composio: {
        type: 'http',
        url: mcp_url,
        headers: { 'x-api-key': COMPOSIO_API_KEY }
      }
    },
    systemPrompt: 'You are a helpful assistant with access to VLM Run tools via Composio.',
    maxTurns: 10,
  };

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

  console.log('\nChat started. Type "exit" to quit.\n');

  let isProcessing = false;

  async function ask(prompt: string) {
    isProcessing = true;
    rl.pause();

    process.stdout.write('Claude is thinking...');
    const stream = query({ prompt, options });

    let firstChunk = true;
    for await (const msg of stream) {
      const content = (msg as any).message?.content || (msg as any).content;
      if (Array.isArray(content)) {
        for (const block of content) {
          if (block.type === 'text' && block.text) {
            if (firstChunk) {
              process.stdout.write('\r\x1b[K');
              process.stdout.write('Claude: ');
              firstChunk = false;
            }
            process.stdout.write(block.text);
          }
        }
      }
    }
    process.stdout.write('\n\n');

    isProcessing = false;
    rl.resume();
    rl.prompt();
  }

  rl.on('line', async (line) => {
    if (isProcessing) return;

    const input = line.trim();
    if (input === 'exit') {
      rl.close();
      process.exit(0);
    }
    if (input) await ask(input);
    else rl.prompt();
  });

  await ask('What can you help me with?');
}

try {
  await chat();
} catch (error) {
  console.error(error);
  process.exit(1);
}

Conclusion

You've successfully built a Claude Agent SDK agent that can interact with VLM Run through Composio's Tool Router.

Key features:

  • Native MCP support through Claude's agent framework
  • Streaming responses for real-time interaction
  • Permission bypass for smooth automated workflows
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.
TOOLS

Supported Tools

Every VLM Run action and event your agent gets out of the box.

Create Skill

Create a reusable skill from exactly one uploaded zip, prompt, or chat session.

Discover Extraction Schemas

List supported structured-extraction domains, or return the full JSON schema for one domain when domain is provided.

Execute Agent

Start a VLM Run agent execution from an existing agent name or inline configuration over multimodal inputs.

Extract Structured JSON

Start structured JSON extraction from images, a document, a video, or audio using a domain, custom schema, or skill.

Find Files

List uploaded files or find one by file ID or MD5 hash.

Find Skills

List VLM Run skills or find one exact skill by ID, name, and optional version.

Get Run

Get the current status and result of one structured-extraction prediction or agent execution; call repeatedly to poll asynchronous work.

List Agents

Return agents available to the connected account for selection before execution.

List Artifacts

List artifact metadata belonging to exactly one chat session or agent execution.

List Runs

List structured-extraction predictions or agent executions for the connected account.

Upload File

Upload a local file to VLM Run for extraction, agent input, or skill creation.

FRAMEWORKS

How to build VLM Run MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use VLM Run MCP with OpenAI Agents SDK

Claude Code logo

Claude Code

Use VLM Run MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use VLM Run MCP with Claude Cowork

Codex logo

Codex

Use VLM Run MCP with Codex

OpenClaw logo

OpenClaw

Use VLM Run MCP with OpenClaw

Hermes logo

Hermes

Use VLM Run MCP with Hermes

CLI logo

CLI

Use VLM Run MCP with CLI

Google ADK logo

Google ADK

Use VLM Run MCP with Google ADK

LangChain logo

LangChain

Use VLM Run MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use VLM Run MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use VLM Run MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use VLM Run MCP with LlamaIndex

CrewAI logo

CrewAI

Use VLM Run MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, you can. Claude Agent SDK 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 VLM Run tools.

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

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