# How to integrate Ritekit MCP with CrewAI ```json { "title": "How to integrate Ritekit MCP with CrewAI", "toolkit": "Ritekit", "toolkit_slug": "ritekit", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/ritekit/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/ritekit/framework/crew-ai.md", "updated_at": "2026-05-12T10:24:14.621Z" } ``` ## Introduction This guide walks you through connecting Ritekit to CrewAI 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 CrewAI 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) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Ritekit connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Ritekit - Build a conversational loop where your agent can execute Ritekit operations ## What is CrewAI? CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews. Key features include: - Agent Roles: Define specialized agents with specific goals and backstories - Task Management: Create tasks with clear descriptions and expected outputs - Crew Orchestration: Combine agents and tasks into collaborative workflows - MCP Integration: Connect to external tools through Model Context Protocol ## 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 and API key - A Ritekit connection authorized in Composio - An OpenAI API key for the CrewAI LLM - Basic familiarity with Python ### 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 **What's happening:** - composio connects your agent to Ritekit via MCP - crewai provides Agent, Task, Crew, and LLM primitives - crewai-tools[mcp] includes MCP helpers - python-dotenv loads environment variables from .env ```bash pip install composio crewai crewai-tools[mcp] python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates with Composio - USER_ID scopes the session to your account - OPENAI_API_KEY lets CrewAI use your chosen OpenAI model ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import dependencies **What's happening:** - CrewAI classes define agents and tasks, and run the workflow - MCPServerHTTP connects the agent to an MCP endpoint - Composio will give you a short lived Ritekit MCP URL ```python import os from composio import Composio from crewai import Agent, Task, Crew from crewai_tools import MCPServerAdapter import dotenv dotenv.load_dotenv() COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set") ``` ### 5. Create a Composio Tool Router session for Ritekit **What's happening:** - You create a Ritekit only session through Composio - Composio returns an MCP HTTP URL that exposes Ritekit tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["ritekit"]) url = session.mcp.url ``` ### 6. Initialize the MCP Server **What's Happening:** - Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication. - MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format. - Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools. - Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops. - Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries. ```python server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users search the internet effectively", backstory="You are a helpful assistant with access to search tools.", tools=tools, verbose=False, max_iter=10, ) ``` ### 7. Create a CLI Chatloop and define the Crew **What's Happening:** - Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable. - Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean. - Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses. - Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context. - Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response. - Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity. ```python print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Complete Code ```python from crewai import Agent, Task, Crew, LLM from crewai_tools import MCPServerAdapter from composio import Composio from dotenv import load_dotenv import os load_dotenv() GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not GOOGLE_API_KEY: raise ValueError("GOOGLE_API_KEY is not set in the environment.") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set in the environment.") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set in the environment.") # Initialize Composio and create a session composio = Composio(api_key=COMPOSIO_API_KEY) session = composio.create( user_id=COMPOSIO_USER_ID, toolkits=["ritekit"], ) url = session.mcp.url # Configure LLM llm = LLM( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY"), ) server_params = { "url": url, "transport": "streamable-http", "headers": {"x-api-key": COMPOSIO_API_KEY}, } with MCPServerAdapter(server_params) as tools: agent = Agent( role="Search Assistant", goal="Help users with internet searches", backstory="You are an expert assistant with access to Composio Search tools.", tools=tools, llm=llm, verbose=False, max_iter=10, ) print("Chat started! Type 'exit' or 'quit' to end.\n") conversation_context = "" while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue conversation_context += f"\nUser: {user_input}\n" print("\nAgent is thinking...\n") task = Task( description=( f"Conversation history:\n{conversation_context}\n\n" f"Current request: {user_input}" ), expected_output="A helpful response addressing the user's request", agent=agent, ) crew = Crew(agents=[agent], tasks=[task], verbose=False) result = crew.kickoff() response = str(result) conversation_context += f"Agent: {response}\n" print(f"Agent: {response}\n") ``` ## Conclusion You now have a CrewAI agent connected to Ritekit through Composio's Tool Router. The agent can perform Ritekit operations through natural language commands. Next steps: - Add role-specific instructions to customize agent behavior - Plug in more toolkits for multi-app workflows - Chain tasks for complex multi-step operations ## 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) ## 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 CrewAI? Yes, you can. CrewAI 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)