# How to integrate Altoviz MCP with Autogen ```json { "title": "How to integrate Altoviz MCP with Autogen", "toolkit": "Altoviz", "toolkit_slug": "altoviz", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/altoviz/framework/autogen", "markdown_url": "https://composio.dev/toolkits/altoviz/framework/autogen.md", "updated_at": "2026-05-06T08:00:13.619Z" } ``` ## Introduction This guide walks you through connecting Altoviz to AutoGen using the Composio tool router. By the end, you'll have a working Altoviz agent that can find customer details by email address, update a client's company information, retrieve current vat rates for invoices through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Altoviz account through Composio's Altoviz MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Altoviz with - [OpenAI Agents SDK](https://composio.dev/toolkits/altoviz/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/altoviz/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/altoviz/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/altoviz/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/altoviz/framework/codex) - [OpenClaw](https://composio.dev/toolkits/altoviz/framework/openclaw) - [Hermes](https://composio.dev/toolkits/altoviz/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/altoviz/framework/cli) - [Google ADK](https://composio.dev/toolkits/altoviz/framework/google-adk) - [LangChain](https://composio.dev/toolkits/altoviz/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/altoviz/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/altoviz/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/altoviz/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/altoviz/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 Altoviz - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Altoviz tools - Run a live chat loop where you ask the agent to perform Altoviz 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 Altoviz MCP server, and what's possible with it? The Altoviz MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Altoviz account. It provides structured and secure access to your billing, invoicing, and customer management data, so your agent can manage products, find customers, update records, and retrieve financial information on your behalf. - Product management and creation: Instruct your agent to create new products, update details, or delete products from your Altoviz catalog with ease. - Customer and contact lookup: Effortlessly find customers or contacts by email, enabling quick access to client details and supporting streamlined communication. - Financial classification and VAT management: Let your agent fetch available classifications and VAT rates, ensuring accurate tax handling and financial document setup. - Unit retrieval for transactions: Retrieve all available measurement units in your system, supporting precise product and invoice management. - Customer information updates: Have your agent modify or update customer records, keeping your business data up-to-date without manual intervention. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `ALTOVIZ_CREATE_PRODUCT` | Create Product | Creates a new product in the altoviz system. this tool allows for the independent creation of a product with all necessary details. | | `ALTOVIZ_DELETE_PRODUCT` | Delete Product | This tool allows you to delete an existing product from altoviz. the action permanently removes the product from the system. | | `ALTOVIZ_FIND_CONTACT` | Find Contact by Email | This tool allows searching for contacts in altoviz using an email address. the action is independently executable and returns contact details if found. | | `ALTOVIZ_FIND_CUSTOMER` | Find Customer by Email | This tool allows you to find a customer in altoviz by their email address. this is a standalone action that doesn't require any resource ids or dependencies on other tools. | | `ALTOVIZ_FIND_PRODUCT` | Find Product by Number | This tool allows users to find products in altoviz using the product number. it retrieves detailed product information including id, name, number, description, price, and other relevant metadata. it is a critical operation for product management in altoviz. | | `ALTOVIZ_GET_CLASSIFICATIONS` | Get Classifications List | This tool retrieves a list of classifications from the altoviz platform. classifications are essential for producing accounting registers from user-created invoices. it can fetch all classifications or filter them by type. | | `ALTOVIZ_GET_UNITS` | Get Units List | This tool retrieves a list of all available units in the altoviz system. units are used for product measurements and quantity specifications in various transactions. | | `ALTOVIZ_GET_VATS` | Get VAT Rates | This tool retrieves a list of all available vat rates from altoviz. it's essential for creating and managing invoices and quotes where vat calculations are required. it supports retrieving vat rates, validating them for different regions, and ensuring correct tax calculations in financial documents. | | `ALTOVIZ_UPDATE_CUSTOMER` | Update Customer Information | This tool updates an existing customer's information in altoviz. it enables modification of various customer details including contact information, company details, and personal information. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Altoviz MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Altoviz. Instead of manually wiring Altoviz 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 Altoviz 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 Altoviz 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 Altoviz 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 Altoviz 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 Altoviz session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["altoviz"] ) 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 Altoviz 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 Altoviz assistant agent with MCP tools agent = AssistantAgent( name="altoviz_assistant", description="An AI assistant that helps with Altoviz 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 Altoviz 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 Altoviz 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 Altoviz session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["altoviz"] ) 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 Altoviz assistant agent with MCP tools agent = AssistantAgent( name="altoviz_assistant", description="An AI assistant that helps with Altoviz 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 Altoviz 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 Altoviz 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 Altoviz, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Altoviz MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/altoviz/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/altoviz/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/altoviz/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/altoviz/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/altoviz/framework/codex) - [OpenClaw](https://composio.dev/toolkits/altoviz/framework/openclaw) - [Hermes](https://composio.dev/toolkits/altoviz/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/altoviz/framework/cli) - [Google ADK](https://composio.dev/toolkits/altoviz/framework/google-adk) - [LangChain](https://composio.dev/toolkits/altoviz/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/altoviz/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/altoviz/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/altoviz/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/altoviz/framework/crew-ai) ## Related Toolkits - [Stripe](https://composio.dev/toolkits/stripe) - Stripe is a global online payments platform offering APIs for managing payments, customers, and subscriptions. Trusted by businesses for secure, efficient, and scalable payment processing worldwide. - [Alpha vantage](https://composio.dev/toolkits/alpha_vantage) - Alpha Vantage is a financial data platform offering real-time and historical stock market APIs. Get instant, reliable access to equities, forex, and technical analysis data for smarter trading decisions. - [Benzinga](https://composio.dev/toolkits/benzinga) - Benzinga provides real-time financial news and data APIs for market coverage. It helps you track breaking news and actionable market insights instantly. - [Brex](https://composio.dev/toolkits/brex) - Brex provides corporate credit cards and spend management tailored for startups and tech businesses. It helps optimize company cash flow, streamline accounting, and accelerate business growth. - [Chaser](https://composio.dev/toolkits/chaser) - Chaser is accounts receivable automation software that sends invoice reminders and helps businesses get paid faster. It streamlines the collections process to save time and improve cash flow. - [Clientary](https://composio.dev/toolkits/clientary) - Clientary is a platform for managing clients, invoices, projects, proposals, and more. It streamlines client work and saves you serious admin time. - [Coinbase](https://composio.dev/toolkits/coinbase) - Coinbase is a platform for buying, selling, and storing cryptocurrency. It makes exchanging and managing crypto simple and secure for everyone. - [Coinranking](https://composio.dev/toolkits/coinranking) - Coinranking is a comprehensive cryptocurrency market data platform offering access to real-time coin prices, market caps, and historical data. Get accurate, up-to-date stats for thousands of digital assets in one place. - [Coupa](https://composio.dev/toolkits/coupa) - Coupa is a business spend management platform for procurement, invoicing, and expenses. It helps organizations streamline purchasing, control costs, and gain complete visibility over financial operations. - [CurrencyScoop](https://composio.dev/toolkits/currencyscoop) - CurrencyScoop is a developer-friendly API for real-time and historical currency exchange rates. Easily access fiat and crypto data for smart, up-to-date financial applications. - [Daffy](https://composio.dev/toolkits/daffy) - Daffy is a modern charitable giving platform with a donor-advised fund. Easily set aside funds, grow them tax-free, and donate to over 1.7 million U.S. charities. - [Eagle doc](https://composio.dev/toolkits/eagle_doc) - Eagle doc is an AI-powered OCR API for invoices and receipts. It delivers fast, reliable, and accurate document data extraction for seamless automation. - [Elorus](https://composio.dev/toolkits/elorus) - Elorus is an online invoicing and time-tracking software for freelancers and small businesses. Easily manage finances, bill clients, and track work in one place. - [Eodhd apis](https://composio.dev/toolkits/eodhd_apis) - Eodhd apis delivers comprehensive financial data, including live and historical stock prices, via robust APIs. Easily access reliable, up-to-date market insights to power your apps, dashboards, and analytics. - [Fidel api](https://composio.dev/toolkits/fidel_api) - Fidel api is a secure platform for linking payment cards to web and mobile apps. It enables real-time card transaction monitoring and event-based automation for businesses. - [Finage](https://composio.dev/toolkits/finage) - Finage is a secure API platform delivering real-time and historical financial data for stocks, forex, crypto, indices, and commodities. It empowers developers and businesses to access, analyze, and act on market data instantly. - [Finmei](https://composio.dev/toolkits/finmei) - Finmei is an invoicing tool that simplifies billing, invoice management, and expense tracking. Ideal for automating and organizing your business finances in one place. - [Fixer](https://composio.dev/toolkits/fixer) - Fixer is a currency data API offering real-time and historical exchange rates for 170 currencies. Instantly access accurate, up-to-date forex data for your applications and workflows. - [Fixer io](https://composio.dev/toolkits/fixer_io) - Fixer.io is a lightweight API for real-time and historical foreign exchange rates. It makes global currency conversion fast, accurate, and hassle-free. - [Flutterwave](https://composio.dev/toolkits/flutterwave) - Flutterwave is a global payments platform enabling businesses to accept and send payments across Africa and beyond. Its robust APIs simplify cross-border transactions and financial operations. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Altoviz MCP? With a standalone Altoviz MCP server, the agents and LLMs can only access a fixed set of Altoviz tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Altoviz 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 Altoviz tools. ### Can I manage the permissions and scopes for Altoviz while using Tool Router? Yes, absolutely. You can configure which Altoviz 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 Altoviz data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) ยท [Composio docs](https://docs.composio.dev/llms.txt)