# How to integrate Gender api MCP with Pydantic AI ```json { "title": "How to integrate Gender api MCP with Pydantic AI", "toolkit": "Gender api", "toolkit_slug": "gender_api", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/gender_api/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/gender_api/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:12:40.040Z" } ``` ## Introduction This guide walks you through connecting Gender api to Pydantic AI 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 Pydantic AI 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: - How to set up your Composio API key and User ID - How to create a Composio Tool Router session for Gender api - 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 Gender api 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 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 agent to Gender api. It provides structured and secure access so your agent can perform Gender api 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 Gender api - 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 Gender api - MCPServerStreamableHTTP connects to the Gender api 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 Gender api 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 Gender api composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["gender_api"], ) 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 Gender api endpoint - The agent uses GPT-5 to interpret user commands and perform Gender api operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent gender_api_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[gender_api_mcp], instructions=( "You are a Gender api assistant. Use Gender api 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 - Gender api 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 Gender api.\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 Gender api composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["gender_api"], ) 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 gender_api_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[gender_api_mcp], instructions=( "You are a Gender api assistant. Use Gender api 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 Gender api.\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 Gender api through Composio's Tool Router. With this setup, your agent can perform real Gender api 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 + Gender api 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 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 detailed environmental insights directly from your own sensors for smarter apps and automations. - [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements. - [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints. - [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots. - [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. 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Instantly enrich your applications with up-to-date insights on 20M+ businesses. - [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. 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. ## 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 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 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)