# How to integrate Anthropic administrator MCP with Autogen

```json
{
  "title": "How to integrate Anthropic administrator MCP with Autogen",
  "toolkit": "Anthropic administrator",
  "toolkit_slug": "anthropic_administrator",
  "framework": "AutoGen",
  "framework_slug": "autogen",
  "url": "https://composio.dev/toolkits/anthropic_administrator/framework/autogen",
  "markdown_url": "https://composio.dev/toolkits/anthropic_administrator/framework/autogen.md",
  "updated_at": "2026-05-12T10:01:16.455Z"
}
```

## Introduction

This guide walks you through connecting Anthropic administrator to AutoGen using the Composio tool router. By the end, you'll have a working Anthropic administrator agent that can list all available anthropic models, get details for a specific anthropic model, cache a frequently used prompt template through natural language commands.
This guide will help you understand how to give your AutoGen agent real control over a Anthropic administrator account through Composio's Anthropic administrator MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Anthropic administrator with

- [ChatGPT](https://composio.dev/toolkits/anthropic_administrator/framework/chatgpt)
- [Antigravity](https://composio.dev/toolkits/anthropic_administrator/framework/antigravity)
- [OpenAI Agents SDK](https://composio.dev/toolkits/anthropic_administrator/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/anthropic_administrator/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/anthropic_administrator/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/anthropic_administrator/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/anthropic_administrator/framework/codex)
- [Cursor](https://composio.dev/toolkits/anthropic_administrator/framework/cursor)
- [VS Code](https://composio.dev/toolkits/anthropic_administrator/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/anthropic_administrator/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/anthropic_administrator/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/anthropic_administrator/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/anthropic_administrator/framework/cli)
- [Google ADK](https://composio.dev/toolkits/anthropic_administrator/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/anthropic_administrator/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/anthropic_administrator/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/anthropic_administrator/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/anthropic_administrator/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/anthropic_administrator/framework/crew-ai)

## 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 Anthropic administrator
- Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
- Configure an Autogen AssistantAgent that can call Anthropic administrator tools
- Run a live chat loop where you ask the agent to perform Anthropic administrator 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 Anthropic administrator MCP server, and what's possible with it?

The Anthropic administrator MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Anthropic administrator account. It provides structured and secure access to your Anthropic organization, so your agent can perform actions like generating model completions, managing models, retrieving model details, and optimizing prompt usage on your behalf.
- Seamless message and completion creation: Instruct your agent to generate assistant responses or create message completions within conversation workflows, tailored to your organizational needs.
- Comprehensive model listing and management: Have your agent fetch a full list of available Anthropic models, making it easy to discover and select the right model for any use case.
- Detailed model information retrieval: Let your agent pull specific details about any Anthropic model by its ID, ensuring you always have the latest information on capabilities and configurations.
- Efficient prompt caching and reuse: Enable your agent to store and reuse portions of prompts, reducing both operational costs and latency for high-frequency or repetitive tasks.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ANTHROPIC_ADMINISTRATOR_CREATE_MESSAGE` | Create Message | Create a message completion from Claude. Send a conversation history with user/assistant messages and receive Claude's response. Useful for: chatbots, Q&A systems, text generation, conversational AI, and any task requiring natural language understanding or generation. |
| `ANTHROPIC_ADMINISTRATOR_GET_MODEL` | Get Model | Tool to retrieve details of a specific model by its ID. Use after confirming the model ID is valid. |
| `ANTHROPIC_ADMINISTRATOR_LIST_MODELS` | List models | Tool to list available models. Use when you need to see which models are available before selection. Always source model IDs from this tool rather than hard-coding them; stale or outdated model IDs passed to CREATE_MESSAGE will cause a not_found_error. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Anthropic administrator MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Anthropic administrator. Instead of manually wiring Anthropic administrator APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

You will need:
- A Composio API key
- An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
- A Anthropic administrator account you can connect to Composio
- Some basic familiarity with Autogen and Python async

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

Install Composio, Autogen extensions, and dotenv.
What's happening:
- composio connects your agent to Anthropic administrator via MCP
- autogen-agentchat provides the AssistantAgent class
- autogen-ext-openai provides the OpenAI model client
- autogen-ext-tools provides MCP workbench support
```bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools
```

### 3. Set up environment variables

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 Anthropic administrator connections to use
```bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com
```

### 4. Import dependencies and create Tool Router session

What's happening:
- load_dotenv() reads your .env file
- Composio(api_key=...) initializes the SDK
- create(...) creates a Tool Router session that exposes Anthropic administrator tools
- session.mcp.url is the MCP endpoint that Autogen will connect to
```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 Anthropic administrator session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["anthropic_administrator"]
    )
    url = session.mcp.url
```

### 5. Configure MCP parameters for Autogen

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
```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")}
)
```

### 6. Create the model client and agent

What's happening:
- OpenAIChatCompletionClient wraps the OpenAI model for Autogen
- McpWorkbench connects the agent to the MCP tools
- AssistantAgent is configured with the Anthropic administrator tools from the workbench
```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 Anthropic administrator assistant agent with MCP tools
    agent = AssistantAgent(
        name="anthropic_administrator_assistant",
        description="An AI assistant that helps with Anthropic administrator operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )
```

### 7. Run the interactive chat loop

What's happening:
- The script prompts you in a loop with You:
- Autogen passes your input to the model, which decides which Anthropic administrator 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
```python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Anthropic administrator 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")
```

## Complete Code

```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 Anthropic administrator session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["anthropic_administrator"]
    )
    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 Anthropic administrator assistant agent with MCP tools
        agent = AssistantAgent(
            name="anthropic_administrator_assistant",
            description="An AI assistant that helps with Anthropic administrator 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 Anthropic administrator 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 Anthropic administrator 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 Anthropic administrator, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

## How to build Anthropic administrator MCP Agent with another framework

- [ChatGPT](https://composio.dev/toolkits/anthropic_administrator/framework/chatgpt)
- [Antigravity](https://composio.dev/toolkits/anthropic_administrator/framework/antigravity)
- [OpenAI Agents SDK](https://composio.dev/toolkits/anthropic_administrator/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/anthropic_administrator/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/anthropic_administrator/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/anthropic_administrator/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/anthropic_administrator/framework/codex)
- [Cursor](https://composio.dev/toolkits/anthropic_administrator/framework/cursor)
- [VS Code](https://composio.dev/toolkits/anthropic_administrator/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/anthropic_administrator/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/anthropic_administrator/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/anthropic_administrator/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/anthropic_administrator/framework/cli)
- [Google ADK](https://composio.dev/toolkits/anthropic_administrator/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/anthropic_administrator/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/anthropic_administrator/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/anthropic_administrator/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/anthropic_administrator/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/anthropic_administrator/framework/crew-ai)

## Related Toolkits

- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - 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.
- [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application.
- [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities.
- [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications.
- [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations.
- [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key.
- [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient.
- [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions.
- [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences.
- [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement.
- [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team.
- [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs.
- [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code.
- [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets.
- [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features.
- [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Anthropic administrator MCP?

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

### Can I use Tool Router MCP with Autogen?

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 Anthropic administrator tools.

### Can I manage the permissions and scopes for Anthropic administrator while using Tool Router?

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

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)