How to integrate Ai ml api MCP with Claude Agent SDK

This guide walks you through connecting Ai ml api to the Claude Agent SDK using the Composio tool router. By the end, you'll have a working Ai ml api agent that can check if this image contains unsafe content, summarize this customer chat conversation, generate a polite reply to this message through natural language commands. This guide will help you understand how to give your Claude Agent SDK agent real control over a Ai ml api account through Composio's Ai ml api MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows.

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Introduction

This guide walks you through connecting Ai ml api to the Claude Agent SDK using the Composio tool router. By the end, you'll have a working Ai ml api agent that can check if this image contains unsafe content, summarize this customer chat conversation, generate a polite reply to this message through natural language commands.

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

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

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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 Ai ml api
  • Configure an AI agent that can use Ai ml api as a tool
  • Run a live chat session where you can ask the agent to perform Ai ml api 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 Ai ml api MCP server, and what's possible with it?

The Ai ml 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 Ai ml api account. It provides structured and secure access to powerful AI/ML models, so your agent can generate text, moderate user content, and automate intelligent workflows on your behalf.

  • Automated content moderation: Instantly classify and filter user-generated text or images using advanced moderation models to keep your platform safe and compliant.
  • Dynamic text generation: Have your agent generate chat responses, write creative copy, or complete conversations using state-of-the-art language models.
  • Context-aware conversation handling: Let your agent analyze conversation history and produce coherent, relevant replies for chatbots or digital assistants.
  • Seamless integration of AI workflows: Combine moderation and text generation tools to build smart, automated pipelines tailored to your product’s needs.

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 Ai ml api 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 Ai ml api 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 Ai ml api
  const session = await composio.create(USER_ID, {
    toolkits: ['ai_ml_api'],
  });
  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 Ai ml api
  • 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 Ai ml api 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 Ai ml api
  • 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 Ai ml api 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: ['ai_ml_api']
  });
  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 Ai ml api 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 Ai ml api 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 Ai ml api action and event your agent gets out of the box.

Cancel Run

Tool to cancel a run that is currently in progress.

Create Assistant

Tool to create an AI assistant with configurable model, instructions, and tools.

Create Message

Tool to create a new message in a thread.

Create Run

Tool to create a run that executes an assistant on a thread.

Create Thread

Tool to create a new thread for conversation with an assistant.

Delete Assistant

Tool to delete an assistant by ID.

Delete Message

Tool to delete a specific message from a thread.

Delete Thread

Tool to delete a thread by its ID.

Get Assistant

Tool to retrieve details of a specific assistant by ID.

Get Billing Balance

Tool to retrieve the current billing balance for the account.

Get Luma Generation

Tool to fetch Luma AI video generation results by generation IDs.

Get Message

Tool to retrieve information about a specific message by its ID.

Get Response by ID

Tool to retrieve a previously generated model response by its unique ID.

Get Run

Tool to retrieve a specific run by ID from a thread.

Get Run Step

Tool to retrieve a specific run step by its ID within a thread and run.

Get Thread

Tool to retrieve information about a specific thread by ID.

List Assistants

Tool to list all assistants associated with the account.

List Batches

Tool to get the status or results of a batch processing job.

List Luma AI Generations

Tool to fetch user's Luma AI video generations.

List Thread Messages

Tool to retrieve a list of messages from a specific thread.

List Models

Tool to list all available AI models from the AI/ML API.

List Models With Details

Tool to list all available AI/ML models with detailed information including pricing, features, and capabilities.

List Runs

Tool to list all runs for a specific thread.

List Run Steps

Tool to list the steps in a run.

Submit Tool Outputs

Tool to submit tool outputs for a run that requires action.

Text Chat Completion

Tool to generate text completions or chat responses using a specified LLM model.

Update Assistant

Tool to modify an existing assistant's properties including name, instructions, model, and tools.

Update Message

Tool to modify metadata for a specific message in a thread.

Update Run

Tool to update a run's metadata with key-value pairs.

Update Thread

Tool to update thread metadata and tool resources in the AI/ML API.

FRAMEWORKS

How to build Ai ml api MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

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Claude Code logo

Claude Code

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Claude Cowork logo

Claude Cowork

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Codex logo

Codex

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OpenClaw logo

OpenClaw

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Hermes logo

Hermes

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CLI logo

CLI

Use Ai ml api MCP with CLI

Google ADK logo

Google ADK

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LangChain logo

LangChain

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Vercel AI SDK logo

Vercel AI SDK

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Mastra AI logo

Mastra AI

Use Ai ml api MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Ai ml api MCP with LlamaIndex

CrewAI logo

CrewAI

Use Ai ml api MCP with CrewAI

FAQ

Frequently asked questions

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

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

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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's immensely helped keep new users on the platform.

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

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

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