# How to integrate Genderapi io MCP with Pydantic AI ```json { "title": "How to integrate Genderapi io MCP with Pydantic AI", "toolkit": "Genderapi io", "toolkit_slug": "genderapi_io", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/genderapi_io/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/genderapi_io/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:12:41.925Z" } ``` ## Introduction This guide walks you through connecting Genderapi io to Pydantic AI using the Composio tool router. By the end, you'll have a working Genderapi io agent that can determine gender from a customer email address, infer gender based on a given username, get gender prediction for full names in a csv through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Genderapi io account through Composio's Genderapi io MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Genderapi io with - [OpenAI Agents SDK](https://composio.dev/toolkits/genderapi_io/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/genderapi_io/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/genderapi_io/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/genderapi_io/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/genderapi_io/framework/codex) - [OpenClaw](https://composio.dev/toolkits/genderapi_io/framework/openclaw) - [Hermes](https://composio.dev/toolkits/genderapi_io/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/genderapi_io/framework/cli) - [Google ADK](https://composio.dev/toolkits/genderapi_io/framework/google-adk) - [LangChain](https://composio.dev/toolkits/genderapi_io/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/genderapi_io/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/genderapi_io/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/genderapi_io/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/genderapi_io/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up your Composio API key and User ID - How to create a Composio Tool Router session for Genderapi io - How to attach an MCP Server to a Pydantic AI agent - How to stream responses and maintain chat history - How to build a simple REPL-style chat interface to test your Genderapi io workflows ## What is Pydantic AI? Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications. Key features include: - Type Safety: Built on Pydantic for automatic data validation - MCP Support: Native support for Model Context Protocol servers - Streaming: Built-in support for streaming responses - Async First: Designed for async/await patterns ## What is the Genderapi io MCP server, and what's possible with it? The Genderapi io MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Genderapi io account. It provides structured and secure access to gender identification services, so your agent can perform actions like inferring gender from names, emails, or usernames, checking usage statistics, and validating API errors on your behalf. - Gender inference from first names: Your agent can determine the likely gender associated with a given first name, supporting localization for more accurate results. - Gender prediction from email addresses: Easily infer gender from a provided email address, enabling smart personalization workflows after obtaining proper consent. - Analyze usernames and full names for gender: Let your agent deduce gender from usernames, nicknames, or full names—even when only partial information is available. - Account usage and API credit monitoring: Check remaining GenderAPI.io credits and expiry dates so you never run out of quota unexpectedly. - Comprehensive error code listing: Retrieve and understand all possible API error codes to streamline debugging and ensure robust integrations. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `GENDERAPI_IO_GENDERAPI_GET_STATS` | Gender API Get Statistics | Tool to retrieve account usage statistics from GenderAPI.io. Use when you need to check remaining API credits and expiry. | | `GENDERAPI_IO_GENDER_API_QUERY_BY_FIRST_NAME` | Query Gender by First Name | Tool to determine gender by querying first name. Use when you need to infer likely gender for a given name with optional localization hints. | | `GENDERAPI_IO_GET_GENDER_FROM_USERNAME` | Get Gender from Username | Tool to determine gender from a username or nickname. Use when you have an alias or handle and want to infer gender from that identifier. | | `GENDERAPI_IO_LIST_ERROR_CODES` | List Gender API Error Codes | Tool to list all possible error codes returned by Gender API. Use when debugging or validating API responses. | | `GENDERAPI_IO_QUERY_BY_EMAIL_ADDRESS` | Query gender by email address | Tool to determine gender from an email address. Use when you need to infer gender for personalization after obtaining proper consent. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Genderapi io MCP server is an implementation of the Model Context Protocol that connects your AI agent to Genderapi io. It provides structured and secure access so your agent can perform Genderapi io operations on your behalf through a secure, permission-based interface. 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 Before starting, make sure you have: - Python 3.9 or higher - A Composio account with an active API key - Basic familiarity with Python and async programming ### 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 the required libraries. What's happening: - composio connects your agent to external SaaS tools like Genderapi io - pydantic-ai lets you create structured AI agents with tool support - python-dotenv loads your environment variables securely from a .env file ```bash pip install composio pydantic-ai python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates your agent to Composio's API - USER_ID associates your session with your account for secure tool access - OPENAI_API_KEY to access OpenAI LLMs ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key ``` ### 4. Import dependencies What's happening: - We load environment variables and import required modules - Composio manages connections to Genderapi io - MCPServerStreamableHTTP connects to the Genderapi io MCP server endpoint - Agent from Pydantic AI lets you define and run the AI assistant ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() ``` ### 5. Create a Tool Router Session What's happening: - We're creating a Tool Router session that gives your agent access to Genderapi io tools - The create method takes the user ID and specifies which toolkits should be available - The returned session.mcp.url is the MCP server URL that your agent will use ```python async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Genderapi io composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["genderapi_io"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") ``` ### 6. Initialize the Pydantic AI Agent What's happening: - The MCP client connects to the Genderapi io endpoint - The agent uses GPT-5 to interpret user commands and perform Genderapi io operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent genderapi_io_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[genderapi_io_mcp], instructions=( "You are a Genderapi io assistant. Use Genderapi io tools to help users " "with their requests. Ask clarifying questions when needed." ), ) ``` ### 7. Build the chat interface What's happening: - The agent reads input from the terminal and streams its response - Genderapi io API calls happen automatically under the hood - The model keeps conversation history to maintain context across turns ```python # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Genderapi io.\n") 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", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() ``` ### 8. Run the application What's happening: - The asyncio loop launches the agent and keeps it running until you exit ```python if __name__ == "__main__": asyncio.run(main()) ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Genderapi io composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["genderapi_io"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") # Attach the MCP server to a Pydantic AI Agent genderapi_io_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[genderapi_io_mcp], instructions=( "You are a Genderapi io assistant. Use Genderapi io tools to help users " "with their requests. Ask clarifying questions when needed." ), ) # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Genderapi io.\n") 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", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You've built a Pydantic AI agent that can interact with Genderapi io through Composio's Tool Router. With this setup, your agent can perform real Genderapi io actions through natural language. You can extend this further by: - Adding other toolkits like Gmail, HubSpot, or Salesforce - Building a web-based chat interface around this agent - Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Genderapi io for workflow automation) This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations. ## How to build Genderapi io MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/genderapi_io/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/genderapi_io/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/genderapi_io/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/genderapi_io/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/genderapi_io/framework/codex) - [OpenClaw](https://composio.dev/toolkits/genderapi_io/framework/openclaw) - [Hermes](https://composio.dev/toolkits/genderapi_io/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/genderapi_io/framework/cli) - [Google ADK](https://composio.dev/toolkits/genderapi_io/framework/google-adk) - [LangChain](https://composio.dev/toolkits/genderapi_io/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/genderapi_io/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/genderapi_io/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/genderapi_io/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/genderapi_io/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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It helps streamline how you gather, organize, and analyze responses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Genderapi io MCP? With a standalone Genderapi io MCP server, the agents and LLMs can only access a fixed set of Genderapi io tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Genderapi io and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with Pydantic AI? Yes, you can. Pydantic AI 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 Genderapi io tools. ### Can I manage the permissions and scopes for Genderapi io while using Tool Router? Yes, absolutely. You can configure which Genderapi io 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 Genderapi io data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)