How to integrate NMKR Studio MCP with Autogen

This guide walks you through connecting NMKR Studio to AutoGen using the Composio tool router. By the end, you'll have a working NMKR Studio agent that can list your nft projects, inspect nfts, check mint prices, validate metadata, and view payout wallets through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a NMKR Studio account through Composio's NMKR Studio MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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NMKR Studio is an NFT platform and API suite for Cardano project creation, asset management, minting, sales, and wallets. Use it to run NFT drops and Cardano asset workflows without building the whole blockchain backend yourself.

11 Tools

Introduction

This guide walks you through connecting NMKR Studio to AutoGen using the Composio tool router. By the end, you'll have a working NMKR Studio agent that can list your nft projects, inspect nfts, check mint prices, validate metadata, and view payout wallets through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a NMKR Studio account through Composio's NMKR Studio MCP server.

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

Also integrate NMKR Studio with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for NMKR Studio
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call NMKR Studio tools
  • Run a live chat loop where you ask the agent to perform NMKR Studio operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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

The NMKR Studio MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your NMKR Studio account. It provides structured and secure access to your projects, NFTs, sales settings, wallet information, and Cardano market data, so your agent can review projects, inspect NFTs, check sale settings, monitor balances, and retrieve exchange rates on your behalf.

  • Project and NFT tracking: Have your agent list your projects, review project details and NFT state counts, and browse NFTs by lifecycle state.
  • NFT details and metadata checks: Direct your agent to retrieve a specific NFT, inspect its stored details, and verify whether its metadata is valid.
  • Pricing and sale reviews: Let the agent check current NFT prices and active sale conditions for your projects.
  • Wallet and mint coupon monitoring: Instruct your agent to list configured payout wallets and check your current Cardano mint coupon balance.
  • Token and exchange rate lookup: Have your agent retrieve blockchain token metadata and check current ADA, BTC, USD, EUR, or JPY conversion rates available through NMKR.

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 step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A NMKR Studio account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
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 dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to NMKR Studio via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's NMKR Studio connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a NMKR Studio session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["nmkr_studio"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes NMKR Studio tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create NMKR Studio assistant agent with MCP tools
    agent = AssistantAgent(
        name="nmkr_studio_assistant",
        description="An AI assistant that helps with NMKR Studio operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the NMKR Studio tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any NMKR Studio related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which NMKR Studio tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with NMKR Studio and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a NMKR Studio session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["nmkr_studio"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create NMKR Studio assistant agent with MCP tools
        agent = AssistantAgent(
            name="nmkr_studio_assistant",
            description="An AI assistant that helps with NMKR Studio operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any NMKR Studio related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into NMKR Studio through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for NMKR Studio, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every NMKR Studio action and event your agent gets out of the box.

Get Exchange Rates

Return NMKR's current BTC, USD, EUR, and JPY conversion rates for a supported coin; omitting coin requests the provider's ADA default.

Get Mint Coupon Balance

Return the connected account's Cardano mint-coupon balance, effective time, and documented mint-purchase address.

Get NFT

Return one NFT's details using either its NMKR NFT UID or the combination of project UID and NFT name.

Get Project

Return details and NFT state counts for an NMKR Studio project identified by its project UID.

Get Project Pricelist

Return the currently valid NFT prices configured for an NMKR Studio project, optionally including all prices exposed by NMKR.

Get Project Sale Conditions

Return the active sale conditions configured for an NMKR Studio project.

Get Token Metadata

Return metadata for a blockchain token identified by policy ID and hex-encoded token name.

List Payout Wallets

Return payout wallets already configured on the connected NMKR Studio account; this tool does not add, validate, or expose wallet secrets.

List Project NFTs

Return one page of NFTs in a project filtered by lifecycle state, with a continuation cursor for additional pages.

List Projects

Return one page of projects owned by the connected NMKR Studio account, including identifiers needed by project and NFT tools.

Validate NFT Metadata

Check whether the metadata already stored for an NFT is valid; this read-only form does not submit or update metadata.

FRAMEWORKS

How to build NMKR Studio MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use NMKR Studio MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use NMKR Studio MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use NMKR Studio MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use NMKR Studio MCP with Claude Cowork

Codex logo

Codex

Use NMKR Studio MCP with Codex

OpenClaw logo

OpenClaw

Use NMKR Studio MCP with OpenClaw

Hermes logo

Hermes

Use NMKR Studio MCP with Hermes

CLI logo

CLI

Use NMKR Studio MCP with CLI

Google ADK logo

Google ADK

Use NMKR Studio MCP with Google ADK

LangChain logo

LangChain

Use NMKR Studio MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use NMKR Studio MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use NMKR Studio MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use NMKR Studio MCP with LlamaIndex

CrewAI logo

CrewAI

Use NMKR Studio MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, you can. Autogen 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 NMKR Studio tools.

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

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