# How to integrate Dictionary api MCP with CrewAI ```json { "title": "How to integrate Dictionary api MCP with CrewAI", "toolkit": "Dictionary api", "toolkit_slug": "dictionary_api", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/dictionary_api/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/dictionary_api/framework/crew-ai.md", "updated_at": "2026-05-12T10:08:42.039Z" } ``` ## Introduction This guide walks you through connecting Dictionary api to CrewAI using the Composio tool router. By the end, you'll have a working Dictionary api agent that can get the definition and synonyms for 'ubiquitous', find the origin and pronunciation of 'serendipity', list antonyms for the word 'ambiguous' through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Dictionary api account through Composio's Dictionary api MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Dictionary api with - [OpenAI Agents SDK](https://composio.dev/toolkits/dictionary_api/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/dictionary_api/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/dictionary_api/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/dictionary_api/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/dictionary_api/framework/codex) - [OpenClaw](https://composio.dev/toolkits/dictionary_api/framework/openclaw) - [Hermes](https://composio.dev/toolkits/dictionary_api/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/dictionary_api/framework/cli) - [Google ADK](https://composio.dev/toolkits/dictionary_api/framework/google-adk) - [LangChain](https://composio.dev/toolkits/dictionary_api/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/dictionary_api/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/dictionary_api/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/dictionary_api/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Dictionary api connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Dictionary api - Build a conversational loop where your agent can execute Dictionary api 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 Dictionary api MCP server, and what's possible with it? The Dictionary 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 Dictionary api account. It provides structured and secure access to comprehensive dictionary and thesaurus data, so your agent can look up definitions, explore word origins, access pronunciations, and discover synonyms or antonyms on your behalf. - Instant word definition lookup: Ask your agent to fetch clear, detailed definitions for any word you encounter. - Etymology and origin insights: Let the agent retrieve the historical roots and language origin of specific words for deeper understanding. - Audio pronunciation access: Have your agent provide accurate audio pronunciations, helping with proper usage and learning. - Synonyms and antonyms discovery: Quickly find synonyms and antonyms to enrich your vocabulary or refine your writing style. - Comprehensive lexical information: Enable your agent to surface all available meanings, usage examples, and nuanced word senses for precise communication. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `DICTIONARY_API_GET_WORD_DEFINITION_V2` | Get Word Definition | Retrieve comprehensive dictionary data for a word including definitions, phonetics, etymology, synonyms, and antonyms. Uses the Free Dictionary API (dictionaryapi.dev) - a free, open-source dictionary powered by Wiktionary data. Returns detailed word information including multiple definitions grouped by part of speech, pronunciation audio, example sentences, and related words. Ideal for language learning, writing assistance, and vocabulary exploration. Note: Primarily supports English ('en'). Other languages may have limited coverage. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Dictionary api MCP server is an implementation of the Model Context Protocol that connects your AI agent to Dictionary api. It provides structured and secure access so your agent can perform Dictionary 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 and API key - A Dictionary api 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 Dictionary api 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 Dictionary api 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 Dictionary api **What's happening:** - You create a Dictionary api only session through Composio - Composio returns an MCP HTTP URL that exposes Dictionary api tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["dictionary_api"]) 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=["dictionary_api"], ) 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 Dictionary api through Composio's Tool Router. The agent can perform Dictionary api 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 Dictionary api MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/dictionary_api/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/dictionary_api/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/dictionary_api/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/dictionary_api/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/dictionary_api/framework/codex) - [OpenClaw](https://composio.dev/toolkits/dictionary_api/framework/openclaw) - [Hermes](https://composio.dev/toolkits/dictionary_api/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/dictionary_api/framework/cli) - [Google ADK](https://composio.dev/toolkits/dictionary_api/framework/google-adk) - [LangChain](https://composio.dev/toolkits/dictionary_api/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/dictionary_api/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/dictionary_api/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/dictionary_api/framework/llama-index) ## Related Toolkits - [Canvas](https://composio.dev/toolkits/canvas) - Canvas is a learning management system for online courses, assignments, grading, and collaboration. 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It lets teams work together from anywhere, updating information instantly. - [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Dictionary api MCP? With a standalone Dictionary api MCP server, the agents and LLMs can only access a fixed set of Dictionary api tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Dictionary api 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 Dictionary api tools. ### Can I manage the permissions and scopes for Dictionary api while using Tool Router? Yes, absolutely. You can configure which Dictionary 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 Dictionary api data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) ยท [Composio docs](https://docs.composio.dev/llms.txt)