# How to integrate Gender api MCP with Autogen ```json { "title": "How to integrate Gender api MCP with Autogen", "toolkit": "Gender api", "toolkit_slug": "gender_api", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/gender_api/framework/autogen", "markdown_url": "https://composio.dev/toolkits/gender_api/framework/autogen.md", "updated_at": "2026-05-12T10:12:40.040Z" } ``` ## Introduction This guide walks you through connecting Gender api to AutoGen using the Composio tool router. By the end, you'll have a working Gender api agent that can guess gender from this customer email address, identify gender by first name in spreadsheet, check your gender api credit balance through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Gender api account through Composio's Gender api MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Gender api with - [OpenAI Agents SDK](https://composio.dev/toolkits/gender_api/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/gender_api/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/gender_api/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/gender_api/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/gender_api/framework/codex) - [OpenClaw](https://composio.dev/toolkits/gender_api/framework/openclaw) - [Hermes](https://composio.dev/toolkits/gender_api/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/gender_api/framework/cli) - [Google ADK](https://composio.dev/toolkits/gender_api/framework/google-adk) - [LangChain](https://composio.dev/toolkits/gender_api/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/gender_api/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/gender_api/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/gender_api/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/gender_api/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 Gender api - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Gender api tools - Run a live chat loop where you ask the agent to perform Gender api 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 Gender api MCP server, and what's possible with it? The Gender 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 Gender api account. It provides structured and secure access to name and email-based gender prediction, so your agent can determine gender from names, emails, usernames, and retrieve related statistics on your behalf. - Predict gender from first names: Instantly infer the likely gender associated with any given first name, streamlining user profiling and personalization tasks. - Determine gender via email address: Allow your agent to analyze an email address and return a best-guess gender, perfect for onboarding flows or marketing segmentation. - Classify gender from full names: Use full name strings to predict gender, even when only a complete name is available—helpful for user enrichment or analytics. - Identify probable country of origin: Retrieve the most likely countries of origin for a given name, adding geographic context to data enrichment and user insights. - Monitor API usage and stats: Let your agent fetch real-time account statistics, including remaining credits and recent usage details, so you can manage integrations efficiently. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `GENDER_API_GENDER_API_GET_COUNTRY_OF_ORIGIN` | Get Country of Origin | Tool to retrieve a name's likely countries of origin. Use after confirming the name identifier. | | `GENDER_API_GET_COUNTRY_OF_ORIGIN_V1` | Get Country of Origin (v1) | Tool to get the country of origin for a given first name using v1 API. Returns the most likely country where the name is most common. | | `GENDER_API_GET_STATISTIC` | Get Gender API Statistics | Tool to retrieve account statistics from Gender-API, including remaining credits and usage details. Use when you need to check your credit balance before performing further gender lookups. | | `GENDER_API_QUERY_BY_EMAIL_ADDRESS` | Query Gender by Email Address | Determine likely gender from an email address by extracting and analyzing the name portion. Returns male/female/unknown with a confidence score. Optionally filter by country for improved accuracy. Check result_found to know if a determination was made; probability indicates confidence level. | | `GENDER_API_QUERY_BY_EMAIL_ADDRESS_MULTIPLE` | Query Gender by Multiple Email Addresses | Determine likely gender for multiple email addresses in a single batch request. Returns male/female/unknown with confidence scores for each email. Use for efficient bulk processing. Each result includes result_found flag and optional probability score. | | `GENDER_API_QUERY_BY_FIRST_NAME` | Gender From First Name | Tool to determine the gender of a first name. Use when you need to identify gender based on a given name. | | `GENDER_API_QUERY_BY_FULL_NAME` | Query Gender by Full Name | Tool to determine gender by splitting a full name. Use when you have an exact full name string and want to infer gender. Slightly less reliable for rare or ambiguous names. | | `GENDER_API_QUERY_GENDER_BY_FIRST_NAME_MULTIPLE` | Query Gender by Multiple First Names | Tool to determine gender for multiple first names in a single batch request. Use when you need to query gender for several names efficiently to save API credits and reduce latency. | | `GENDER_API_QUERY_GENDER_BY_FULL_NAME_MULTIPLE` | Query Gender by Full Name (Multiple) | Tool to batch query gender for multiple full names in a single request. Use when you need to determine gender for multiple names efficiently. Each name can optionally include country, locale, or IP for more accurate regional inference. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Gender api MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Gender api. Instead of manually wiring Gender api 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 Gender api 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 Gender api 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 Gender api 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 Gender api 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 Gender api session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["gender_api"] ) 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 Gender api 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 Gender api assistant agent with MCP tools agent = AssistantAgent( name="gender_api_assistant", description="An AI assistant that helps with Gender api 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 Gender api 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 Gender api 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 Gender api session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["gender_api"] ) 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 Gender api assistant agent with MCP tools agent = AssistantAgent( name="gender_api_assistant", description="An AI assistant that helps with Gender api 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 Gender api 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 Gender api 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 Gender api, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Gender api MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/gender_api/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/gender_api/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/gender_api/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/gender_api/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/gender_api/framework/codex) - [OpenClaw](https://composio.dev/toolkits/gender_api/framework/openclaw) - [Hermes](https://composio.dev/toolkits/gender_api/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/gender_api/framework/cli) - [Google ADK](https://composio.dev/toolkits/gender_api/framework/google-adk) - [LangChain](https://composio.dev/toolkits/gender_api/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/gender_api/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/gender_api/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/gender_api/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/gender_api/framework/crew-ai) ## Related Toolkits - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. 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Get unified, reliable access to complex on-chain data for analytics, trading, and research. - [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction. - [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation. - [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation. - [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Gender api MCP? With a standalone Gender api MCP server, the agents and LLMs can only access a fixed set of Gender api tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Gender api 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 Gender api tools. ### Can I manage the permissions and scopes for Gender api while using Tool Router? Yes, absolutely. You can configure which Gender 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. ### 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 Gender api data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)