# How to integrate Metatextai MCP with Autogen ```json { "title": "How to integrate Metatextai MCP with Autogen", "toolkit": "Metatextai", "toolkit_slug": "metatextai", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/metatextai/framework/autogen", "markdown_url": "https://composio.dev/toolkits/metatextai/framework/autogen.md", "updated_at": "2026-05-12T10:18:53.195Z" } ``` ## Introduction This guide walks you through connecting Metatextai to AutoGen using the Composio tool router. By the end, you'll have a working Metatextai agent that can analyze sentiment of product reviews, summarize lengthy customer feedback emails, detect inappropriate language in forum posts through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Metatextai account through Composio's Metatextai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Metatextai with - [OpenAI Agents SDK](https://composio.dev/toolkits/metatextai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/metatextai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/metatextai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/metatextai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/metatextai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/metatextai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/metatextai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/metatextai/framework/cli) - [Google ADK](https://composio.dev/toolkits/metatextai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/metatextai/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/metatextai/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/metatextai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/metatextai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/metatextai/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 Metatextai - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Metatextai tools - Run a live chat loop where you ask the agent to perform Metatextai 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 Metatextai MCP server, and what's possible with it? The Metatextai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Metatextai account. It provides structured and secure access to powerful NLP and text generation features, so your agent can perform actions like generating high-quality text, analyzing sentiment, summarizing content, and moderating language on your behalf. - Automated content generation: Instruct your agent to craft articles, blogs, product descriptions, or custom text for any scenario with advanced language models. - Sentiment analysis: Let your agent determine the emotional tone of emails, chats, reviews, or social posts to gauge customer or audience sentiment. - Content moderation: Ask your agent to scan and flag inappropriate, toxic, or policy-violating language across user-generated content streams. - Summarization and text condensation: Have your agent summarize lengthy documents, emails, or reports into concise, actionable highlights. - Intent and keyword extraction: Enable your agent to quickly identify key topics, intents, or action items within conversations or documents for smarter automation. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `METATEXTAI_CHAT_COMPLETIONS` | Chat Completions | Tool to generate chat completions. Use when you need OpenAI-compatible conversational responses. | | `METATEXTAI_CLASSIFY` | Classify Text | Tool to classify text. Use when you need to obtain labels and confidence scores from a trained MetatextAI model for given text. | | `METATEXTAI_CREATE_POLICY_GUARDRAILS` | Create Policy Guardrails | Tool to create a policy guardrail. Use when you need to define automated guardrails for content in a specific application. | | `METATEXTAI_DELETE_POLICY_GUARDRAILS` | Delete Guardrail Policy | Tool to delete a guardrail policy. Use when you need to remove a policy by ID for a specific application after confirming valid application and policy IDs. | | `METATEXTAI_EVALUATE` | Evaluate Messages | Tool to evaluate LLM messages against policies/guardrails. Use after generating model output to get violation details or corrections. | | `METATEXTAI_EXTRACT` | Run Extraction | Tool to run information extraction. Use when you need to extract structured data from text. | | `METATEXTAI_GENERATE` | Generate Text | Tool to generate text for a project model. Use when you need LLM completions or chat responses. Supports both prompt and message-based inputs with temperature, stop-sequence, and token limits. | | `METATEXTAI_LIST_APPLICATIONS` | List Applications | Tool to retrieve a list of all existing applications. Use when you need to view application IDs, names, and descriptions. | | `METATEXTAI_LIST_MODELS` | List Models | Tool to retrieve a list of all available models and their supported tasks. Use when you need to choose an appropriate model for chat completions. | | `METATEXTAI_LIST_POLICIES_GUARDRAILS` | List Guardrail Policies | Tool to list all guardrail policies for a specific application. Use after obtaining an application ID to inspect its configured policies. | | `METATEXTAI_LIST_RED_TEAM_TEST_PROBES` | List Red Team Test Probes | Tool to list all available red team test probes. Use when you need to discover available probes for red teaming. | | `METATEXTAI_RUN_RED_TEAM_TEST_SCAN` | Run Red Team Test Scan | Tool to run a vulnerability red-team test scan. Use when you need to execute probes against an application. | | `METATEXTAI_UPDATE_POLICY_GUARDRAILS` | Update Policy Guardrails | Tool to update an existing policy's guardrails. Use when you need to modify a policy's rules after confirming it exists. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Metatextai MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Metatextai. Instead of manually wiring Metatextai 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 Metatextai 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 Metatextai 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 Metatextai 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 Metatextai 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 Metatextai session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["metatextai"] ) 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 Metatextai 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 Metatextai assistant agent with MCP tools agent = AssistantAgent( name="metatextai_assistant", description="An AI assistant that helps with Metatextai 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 Metatextai 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 Metatextai 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 Metatextai session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["metatextai"] ) 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 Metatextai assistant agent with MCP tools agent = AssistantAgent( name="metatextai_assistant", description="An AI assistant that helps with Metatextai 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 Metatextai 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 Metatextai 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 Metatextai, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Metatextai MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/metatextai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/metatextai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/metatextai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/metatextai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/metatextai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/metatextai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/metatextai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/metatextai/framework/cli) - [Google ADK](https://composio.dev/toolkits/metatextai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/metatextai/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/metatextai/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/metatextai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/metatextai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/metatextai/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. - [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization. - [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. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Metatextai MCP? With a standalone Metatextai MCP server, the agents and LLMs can only access a fixed set of Metatextai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Metatextai 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 Metatextai tools. ### Can I manage the permissions and scopes for Metatextai while using Tool Router? Yes, absolutely. You can configure which Metatextai 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 Metatextai data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)