# How to integrate Convex MCP with Google ADK ```json { "title": "How to integrate Convex MCP with Google ADK", "toolkit": "Convex", "toolkit_slug": "convex", "framework": "Google ADK", "framework_slug": "google-adk", "url": "https://composio.dev/toolkits/convex/framework/google-adk", "markdown_url": "https://composio.dev/toolkits/convex/framework/google-adk.md", "updated_at": "2026-06-18T09:21:44.444Z" } ``` ## Introduction This guide walks you through connecting Convex to Google ADK 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 Google ADK 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) - [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) - [CrewAI](https://composio.dev/toolkits/convex/framework/crew-ai) ## TL;DR Here's what you'll learn: - Get a Convex account set up and connected to Composio - Install the Google ADK and Composio packages - Create a Composio Tool Router session for Convex - Build an agent that connects to Convex through MCP - Interact with Convex using natural language ## What is Google ADK? Google ADK (Agents Development Kit) is Google's framework for building AI agents powered by Gemini models. It provides tools for creating agents that can use external services through the Model Context Protocol. Key features include: - Gemini Integration: Native support for Google's Gemini models - MCP Toolset: Built-in support for Model Context Protocol tools - Streamable HTTP: Connect to external services through streamable HTTP - CLI and Web UI: Run agents via command line or web interface ## 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: - A Google API key for Gemini models - A Composio account and API key - Python 3.9 or later installed - Basic familiarity with Python ### 1. Getting API Keys for Google and Composio Google API Key - Go to [Google AI Studio](https://aistudio.google.com/app/apikey) and create an API key. - Copy the key and keep it safe. You will put this in GOOGLE_API_KEY. Composio API Key and User ID - Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Go to Settings → API Keys and copy your Composio API key. Use this for COMPOSIO_API_KEY. - Decide on a stable user identifier to scope sessions, often your email or a user ID. Use this for COMPOSIO_USER_ID. ### 2. Install dependencies Inside your virtual environment, install the required packages. What's happening: - google-adk is Google's Agents Development Kit - composio connects your agent to Convex via MCP - python-dotenv loads environment variables ```bash pip install google-adk composio python-dotenv ``` ### 3. Set up ADK project Set up a new Google ADK project. What's happening: - This creates an agent folder with a root agent file and .env file ```bash adk create my_agent ``` ### 4. Set environment variables Save all your credentials in the .env file. What's happening: - GOOGLE_API_KEY authenticates with Google's Gemini models - COMPOSIO_API_KEY authenticates with Composio - COMPOSIO_USER_ID identifies the user for session management ```bash GOOGLE_API_KEY=your-google-api-key COMPOSIO_API_KEY=your-composio-api-key COMPOSIO_USER_ID=your-user-id-or-email ``` ### 5. Import modules and validate environment What's happening: - os reads environment variables - Composio is the main Composio SDK client - GoogleProvider declares that you are using Google ADK as the agent runtime - Agent is the Google ADK LLM agent class - McpToolset lets the ADK agent call MCP tools over HTTP ```python import os import warnings from composio import Composio from dotenv import load_dotenv from google.adk.agents.llm_agent import Agent from google.adk.tools.mcp_tool.mcp_session_manager import StreamableHTTPConnectionParams from google.adk.tools.mcp_tool.mcp_toolset import McpToolset load_dotenv() warnings.filterwarnings("ignore", message=".*BaseAuthenticatedTool.*") 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.") ``` ### 6. Create Composio client and Tool Router session What's happening: - Authenticates to Composio with your API key - Declares Google ADK as the provider - Spins up a short-lived MCP endpoint for your user and selected toolkit - Stores the MCP HTTP URL for the ADK MCP integration ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) composio_session = composio_client.create( user_id=COMPOSIO_USER_ID, toolkits=["convex"], ) COMPOSIO_MCP_URL = composio_session.mcp.url, print(f"Composio MCP URL: {COMPOSIO_MCP_URL}") ``` ### 7. Set up the McpToolset and create the Agent What's happening: - Connects the ADK agent to the Composio MCP endpoint through McpToolset - Uses Gemini as the model powering the agent - Lists exact tool names in instruction to reduce misnamed tool calls ```python composio_toolset = McpToolset( connection_params=StreamableHTTPConnectionParams( url=COMPOSIO_MCP_URL, headers={"x-api-key": COMPOSIO_API_KEY} ) ) root_agent = Agent( model="gemini-2.5-flash", name="composio_agent", description="An agent that uses Composio tools to perform actions.", instruction=( "You are a helpful assistant connected to Composio. " "You have the following tools available: " "COMPOSIO_SEARCH_TOOLS, COMPOSIO_MULTI_EXECUTE_TOOL, " "COMPOSIO_MANAGE_CONNECTIONS, COMPOSIO_REMOTE_BASH_TOOL, COMPOSIO_REMOTE_WORKBENCH. " "Use these tools to help users with Convex operations." ), tools=[composio_toolset], ) print("\nAgent setup complete. You can now run this agent directly ;)") ``` ### 8. Run the agent Execute the agent from the project root. The web command opens a web portal where you can chat with the agent. What's happening: - adk run runs the agent in CLI mode - adk web . opens a web UI for interactive testing ```bash # Run in CLI mode adk run my_agent # Or run in web UI mode adk web ``` ## Complete Code ```python import os import warnings from composio import Composio from composio_google import GoogleProvider from dotenv import load_dotenv from google.adk.agents.llm_agent import Agent from google.adk.tools.mcp_tool.mcp_session_manager import StreamableHTTPConnectionParams from google.adk.tools.mcp_tool.mcp_toolset import McpToolset load_dotenv() warnings.filterwarnings("ignore", message=".*BaseAuthenticatedTool.*") 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.") composio_client = Composio(api_key=COMPOSIO_API_KEY, provider=GoogleProvider()) composio_session = composio_client.create( user_id=COMPOSIO_USER_ID, toolkits=["convex"], ) COMPOSIO_MCP_URL = composio_session.mcp.url composio_toolset = McpToolset( connection_params=StreamableHTTPConnectionParams( url=COMPOSIO_MCP_URL, headers={"x-api-key": COMPOSIO_API_KEY} ) ) root_agent = Agent( model="gemini-2.5-flash", name="composio_agent", description="An agent that uses Composio tools to perform actions.", instruction=( "You are a helpful assistant connected to Composio. " "You have the following tools available: " "COMPOSIO_SEARCH_TOOLS, COMPOSIO_MULTI_EXECUTE_TOOL, " "COMPOSIO_MANAGE_CONNECTIONS, COMPOSIO_REMOTE_BASH_TOOL, COMPOSIO_REMOTE_WORKBENCH. " "Use these tools to help users with Convex operations." ), tools=[composio_toolset], ) print("\nAgent setup complete. You can now run this agent directly ;)") ``` ## Conclusion You've successfully integrated Convex with the Google ADK through Composio's MCP Tool Router. Your agent can now interact with Convex using natural language commands. Key takeaways: - The Tool Router approach dynamically routes requests to the appropriate Convex tools - Environment variables keep your credentials secure and separate from code - Clear agent instructions reduce tool calling errors - The ADK web UI provides an interactive interface for testing and development You can extend this setup by adding more toolkits to the toolkits array in your session configuration. ## 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) - [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) - [CrewAI](https://composio.dev/toolkits/convex/framework/crew-ai) ## 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 Google ADK? Yes, you can. Google ADK 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) · [Composio docs](https://docs.composio.dev/llms.txt)