# How to integrate Convex MCP with CrewAI ```json { "title": "How to integrate Convex MCP with CrewAI", "toolkit": "Convex", "toolkit_slug": "convex", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/convex/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/convex/framework/crew-ai.md", "updated_at": "2026-06-18T09:21:44.444Z" } ``` ## Introduction This guide walks you through connecting Convex to CrewAI using the Composio tool router. By the end, you'll have a working Convex agent that can list records from convex tasks table, run convex query for active users, inspect convex deployment function logs through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Convex account through Composio's Convex MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Convex with - [OpenAI Agents SDK](https://composio.dev/toolkits/convex/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/convex/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/convex/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/convex/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/convex/framework/codex) - [OpenClaw](https://composio.dev/toolkits/convex/framework/openclaw) - [Hermes](https://composio.dev/toolkits/convex/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/convex/framework/cli) - [Google ADK](https://composio.dev/toolkits/convex/framework/google-adk) - [LangChain](https://composio.dev/toolkits/convex/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/convex/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/convex/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/convex/framework/llama-index) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Convex connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Convex - Build a conversational loop where your agent can execute Convex 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 Convex MCP server, and what's possible with it? The Convex MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Convex account. It provides structured and secure access so your agent can perform Convex operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `CONVEX_CREATE_DEPLOY_KEY` | Create deploy key | Tool to create a deploy key for use with the Convex CLI. Use when you need to generate credentials for CLI-based development or deployment workflows. The generated key provides administrative access to the specified deployment. | | `CONVEX_CREATE_DEPLOYMENT` | Create Deployment | Tool to create a new deployment for a Convex project. Use when you need to create a development, production, or custom deployment. Specify the deployment type and optional configuration like class, reference, and region. | | `CONVEX_CREATE_PROJECT` | Create Project | Tool to create a new project on a Convex team, optionally provisioning a dev or prod deployment. Use when you need to initialize a new Convex project in a team. | | `CONVEX_DELETE_CUSTOM_DOMAIN` | Delete Custom Domain | Tool to remove a custom domain from a Convex deployment. Use when you need to delete a previously configured custom domain. | | `CONVEX_DELETE_DEPLOYMENT` | Delete Deployment | Tool to delete a Convex deployment. Use when you need to permanently remove a deployment and all its data. WARNING: This action will delete all data and files in the deployment and cannot be undone. | | `CONVEX_DELETE_PROJECT` | Delete project | Deletes a Convex project and all its deployments permanently. Use when you need to permanently remove a project and all associated data. This operation cannot be undone. | | `CONVEX_EXECUTE_QUERY_BATCH` | Execute Query Batch | Tool to execute multiple Convex query functions in a single batch request. Use when you need to fetch data from multiple queries efficiently in one API call. | | `CONVEX_GET_DEPLOYMENT` | Get Deployment Details | Tool to retrieve details about a Convex cloud deployment. Use when you need to get information about a specific deployment including its configuration, region, creation time, and status. | | `CONVEX_GET_PROJECT_BY_ID` | Get Project by ID | Tool to retrieve detailed information about a specific Convex project by its ID. Use when you need to fetch project metadata including name, slug, team association, and creation time. | | `CONVEX_GET_PROJECT_BY_SLUG` | Get Project by Slug | Tool to retrieve a Convex project by its slug within a team. Use when you need to fetch project details using human-readable identifiers instead of numeric IDs. | | `CONVEX_GET_QUERY_TIMESTAMP` | Get Query Timestamp | Tool to get the latest timestamp for queries from Convex deployment. Use when you need to retrieve the current query timestamp from the Convex API. | | `CONVEX_GET_TOKEN_DETAILS` | Get token details | Tool to retrieve token details for the authenticated token. Returns the team ID for team tokens or project ID for project tokens. Especially useful after receiving a token from an OAuth flow to identify which team or project it belongs to. | | `CONVEX_LIST_DEPLOY_KEYS` | List Deploy Keys | Tool to list all deploy keys for a specified Convex deployment. Use when you need to view all authentication tokens that can be used to deploy to this deployment. | | `CONVEX_LIST_DEPLOYMENT_CLASSES` | List deployment classes | Tool to list available deployment classes for a Convex team. Use when you need to check which deployment classes are available for a specific team. | | `CONVEX_LIST_DEPLOYMENT_REGIONS` | List deployment regions | Tool to list available deployment regions for a Convex team. Use when you need to check which regions are available for deploying a team's backend. | | `CONVEX_LIST_DEPLOYMENTS` | List Deployments | Tool to list all deployments for a Convex project. Use when you need to see all deployments (production, preview, or local) for a specific project. | | `CONVEX_LIST_LOG_STREAMS` | List Log Streams | Tool to list all existing log stream configurations in a deployment. Use when you need to view configured log streaming destinations like Datadog, Webhook, Axiom, or Sentry. | | `CONVEX_LIST_PROJECTS` | List Projects | Tool to list all projects for a specific Convex team. Use when you need to retrieve all projects associated with a team by team ID. | | `CONVEX_UPDATE_DEPLOYMENT` | Update Deployment | Tool to update properties of an existing Convex deployment. Use when you need to modify deployment settings such as dashboard edit confirmation or deployment reference. Only the fields provided in the request are modified. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Convex MCP server is an implementation of the Model Context Protocol that connects your AI agent to Convex. It provides structured and secure access so your agent can perform Convex 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 Convex 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 Convex 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 Convex 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 Convex **What's happening:** - You create a Convex only session through Composio - Composio returns an MCP HTTP URL that exposes Convex tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["convex"]) 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=["convex"], ) 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 Convex through Composio's Tool Router. The agent can perform Convex 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 Convex MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/convex/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/convex/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/convex/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/convex/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/convex/framework/codex) - [OpenClaw](https://composio.dev/toolkits/convex/framework/openclaw) - [Hermes](https://composio.dev/toolkits/convex/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/convex/framework/cli) - [Google ADK](https://composio.dev/toolkits/convex/framework/google-adk) - [LangChain](https://composio.dev/toolkits/convex/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/convex/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/convex/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/convex/framework/llama-index) ## Related Toolkits - [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. - [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms. - [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place. - [1password](https://composio.dev/toolkits/_1password) - 1Password is a password manager and digital vault for storing logins, secrets, notes, and secure documents. It helps individuals and teams protect credentials, share access safely, and reduce password risk. - [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. 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It accelerates development and iteration by combining hosting, database management, and low-code features in one place. - [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines. - [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure. - [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code. - [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows. - [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights. - [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews. - [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Convex MCP? With a standalone Convex MCP server, the agents and LLMs can only access a fixed set of Convex tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Convex 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 Convex tools. ### Can I manage the permissions and scopes for Convex while using Tool Router? Yes, absolutely. You can configure which Convex 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 Convex 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)