# How to integrate Ritekit MCP with Pydantic AI ```json { "title": "How to integrate Ritekit MCP with Pydantic AI", "toolkit": "Ritekit", "toolkit_slug": "ritekit", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/ritekit/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/ritekit/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:24:14.621Z" } ``` ## Introduction This guide walks you through connecting Ritekit to Pydantic AI using the Composio tool router. By the end, you'll have a working Ritekit agent that can suggest hashtags for your blog post draft, check if these instagram hashtags are banned, analyze hashtag stats for marketing campaign through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Ritekit account through Composio's Ritekit MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Ritekit with - [OpenAI Agents SDK](https://composio.dev/toolkits/ritekit/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/ritekit/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/ritekit/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/ritekit/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/ritekit/framework/codex) - [OpenClaw](https://composio.dev/toolkits/ritekit/framework/openclaw) - [Hermes](https://composio.dev/toolkits/ritekit/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/ritekit/framework/cli) - [Google ADK](https://composio.dev/toolkits/ritekit/framework/google-adk) - [LangChain](https://composio.dev/toolkits/ritekit/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/ritekit/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/ritekit/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/ritekit/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/ritekit/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 Ritekit - 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 Ritekit 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 Ritekit MCP server, and what's possible with it? The Ritekit MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ritekit account. It provides structured and secure access to Ritekit’s social media optimization tools, so your agent can generate hashtags, analyze links, validate email addresses, and boost content engagement automatically on your behalf. - Smart hashtag generation and suggestions: Instantly get relevant and trending hashtags for any post or campaign to maximize visibility and reach. - Banned hashtag detection for Instagram: Automatically filter out banned or unsafe hashtags before publishing to keep your posts compliant and effective. - Comprehensive hashtag analytics: Retrieve real-time engagement stats on up to 100 hashtags, including metrics like tweets, retweets, exposure, and popularity grades. - Email address validation: Have your agent detect disposable or free email addresses to improve lead quality and reduce spam signups. - Link ad management: Enable deletion of link ads directly through your agent to keep your promotional content up to date and relevant. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `RITEKIT_AUTO_HASHTAG` | Auto Hashtag | Tool to automatically add relevant hashtags to a given post. Use when you have plain text and need suggested hashtags appended or inserted in context. | | `RITEKIT_BANNED_INSTAGRAM_HASHTAGS` | Check Banned Instagram Hashtags | Tool to identify which hashtags are banned on Instagram. Use when preparing content and need to filter out banned hashtags before posting. | | `RITEKIT_DETECT_DISPOSABLE_EMAIL` | Detect Disposable Email | Tool to detect if an email address is disposable. Use when validating email addresses to filter out temporary or fake email services. | | `RITEKIT_DETECT_EMAIL_TYPO` | Detect Email Typo | Tool to detect common typos in email addresses and suggest corrections. Use when validating email input to help users correct mistakes like gml.com -> gmail.com. | | `RITEKIT_FREEMAIL_DETECTION` | Free Email Detection | Tool to detect whether an email address belongs to a free email provider. Use when validating lead quality before ingestion. | | `RITEKIT_GET_ACCESS_TOKEN` | Get Access Token | Tool to obtain a RiteKit access token. Prefer using a stored token from connection metadata or request. Falls back to OAuth2 client credentials if both client_id and client_secret are provided and no token is otherwise available. | | `RITEKIT_GET_CLIENT_ID` | RiteKit Get Client ID | Tool to retrieve stored RiteKit client_id. Use when child actions require the client_id query parameter. | | `RITEKIT_GET_CLIENT_SECRET` | RiteKit Get Client Secret | Tool to retrieve stored RiteKit client_secret. Use when child actions require the client_secret parameter. | | `RITEKIT_GET_FULL_EMAIL_INSIGHTS` | Get Full Email Insights | Tool to retrieve comprehensive email address insights including full name, free mail detection, business email detection, and typo suggestions. Use when you need detailed analysis of an email address for lead qualification or email validation. | | `RITEKIT_HASHTAG_SUGGESTIONS` | RiteKit Hashtag Suggestions | Tool to get hashtag suggestions for a given text. Use when you need relevant hashtags for social media posts. | | `RITEKIT_LINK_AD_DELETE` | Delete Link Ad | Tool to delete a link ad. Use when you need to permanently remove a link ad by its ID. | | `RITEKIT_LIST_LINK_ADS` | List Link Ads | Tool to retrieve a list of link ads. Use after authenticating to fetch all link ads for the user. | | `RITEKIT_SHORTEN_LINK` | Shorten Link | Tool to shorten a URL with a specified CTA. Use when you need to generate a call-to-action-enabled short link. | | `RITEKIT_TEXT_TO_IMAGE` | Convert Text to Image | Tool to convert a quote into a styled image. Use after preparing quote text and style options. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Ritekit MCP server is an implementation of the Model Context Protocol that connects your AI agent to Ritekit. It provides structured and secure access so your agent can perform Ritekit 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 Ritekit - 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 Ritekit - MCPServerStreamableHTTP connects to the Ritekit 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 Ritekit 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 Ritekit composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["ritekit"], ) 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 Ritekit endpoint - The agent uses GPT-5 to interpret user commands and perform Ritekit operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent ritekit_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[ritekit_mcp], instructions=( "You are a Ritekit assistant. Use Ritekit 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 - Ritekit 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 Ritekit.\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 Ritekit composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["ritekit"], ) 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 ritekit_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[ritekit_mcp], instructions=( "You are a Ritekit assistant. Use Ritekit 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 Ritekit.\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 Ritekit through Composio's Tool Router. With this setup, your agent can perform real Ritekit 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 + Ritekit 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 Ritekit MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/ritekit/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/ritekit/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/ritekit/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/ritekit/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/ritekit/framework/codex) - [OpenClaw](https://composio.dev/toolkits/ritekit/framework/openclaw) - [Hermes](https://composio.dev/toolkits/ritekit/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/ritekit/framework/cli) - [Google ADK](https://composio.dev/toolkits/ritekit/framework/google-adk) - [LangChain](https://composio.dev/toolkits/ritekit/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/ritekit/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/ritekit/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/ritekit/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/ritekit/framework/crew-ai) ## Related Toolkits - [Reddit](https://composio.dev/toolkits/reddit) - Reddit is a social news platform with thriving user-driven communities (subreddits). It's the go-to place for discussion, content sharing, and viral marketing. - [Facebook](https://composio.dev/toolkits/facebook) - Facebook is a social media and advertising platform for businesses and creators. It helps you connect, share, and manage content across your public Facebook Pages. - [Linkedin](https://composio.dev/toolkits/linkedin) - LinkedIn is a professional networking platform for connecting, sharing content, and engaging with business opportunities. It's the go-to place for building your professional brand and unlocking new career connections. - [Active campaign](https://composio.dev/toolkits/active_campaign) - ActiveCampaign is a marketing automation and CRM platform for managing email campaigns, sales pipelines, and customer segmentation. It helps businesses engage customers and drive growth through smart automation and targeted outreach. - [ActiveTrail](https://composio.dev/toolkits/active_trail) - ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease. - [Ahrefs](https://composio.dev/toolkits/ahrefs) - Ahrefs is an SEO and marketing platform for site audits, keyword research, and competitor insights. It helps you improve search rankings and drive organic traffic. - [Amcards](https://composio.dev/toolkits/amcards) - AMCards lets you create and mail personalized greeting cards online. Build stronger customer relationships with easy, automated card campaigns. - [Beamer](https://composio.dev/toolkits/beamer) - Beamer is a news and changelog platform for in-app announcements and feature updates. It helps companies boost user engagement by sharing news where users are most active. - [Benchmark email](https://composio.dev/toolkits/benchmark_email) - Benchmark Email is a platform for creating, sending, and tracking email campaigns. It's built to help you engage audiences and analyze results—all in one place. - [Bigmailer](https://composio.dev/toolkits/bigmailer) - BigMailer is an email marketing platform for managing multiple brands with white-labeling and automation. It helps teams streamline campaigns and simplify integration with Amazon SES. - [Brandfetch](https://composio.dev/toolkits/brandfetch) - Brandfetch is an API that delivers company logos, colors, and visual branding assets. It helps marketers and developers keep brand visuals consistent everywhere. - [Brevo](https://composio.dev/toolkits/brevo) - Brevo is an all-in-one email and SMS marketing platform for transactional messaging, automation, and CRM. It helps businesses engage customers and streamline communications through powerful campaign tools. - [Campayn](https://composio.dev/toolkits/campayn) - Campayn is an email marketing platform for creating, sending, and managing campaigns. It helps businesses engage contacts and grow audiences with easy-to-use tools. - [Cardly](https://composio.dev/toolkits/cardly) - Cardly is a platform for creating and sending personalized direct mail to customers. It helps businesses break through the digital clutter by getting real engagement via physical mailboxes. - [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management. - [Crustdata](https://composio.dev/toolkits/crustdata) - CrustData is an AI-powered data intelligence platform for real-time company and people data. It helps B2B sales teams, AI SDRs, and investors react to live business signals. - [Curated](https://composio.dev/toolkits/curated) - Curated is a platform for collecting, curating, and publishing newsletters. It streamlines content aggregation and distribution for creators and teams. - [Customerio](https://composio.dev/toolkits/customerio) - Customer.io is a customer engagement platform for targeted messaging across email, SMS, and push. Easily automate, segment, and track communications with your audience. - [Cutt ly](https://composio.dev/toolkits/cutt_ly) - Cutt.ly is a URL shortening service for managing and analyzing links. Streamline your workflows with quick, trackable, and branded short URLs. - [Demio](https://composio.dev/toolkits/demio) - Demio is webinar software built for marketers, offering both live and automated sessions with interactive features. It helps teams engage audiences and optimize lead generation through detailed analytics. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Ritekit MCP? With a standalone Ritekit MCP server, the agents and LLMs can only access a fixed set of Ritekit tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Ritekit 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 Ritekit tools. ### Can I manage the permissions and scopes for Ritekit while using Tool Router? Yes, absolutely. You can configure which Ritekit 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 Ritekit data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)