# How to integrate CrowTerminal MCP with Vercel AI SDK v6 ```json { "title": "How to integrate CrowTerminal MCP with Vercel AI SDK v6", "toolkit": "CrowTerminal", "toolkit_slug": "crowterminal", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/crowterminal/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/crowterminal/framework/ai-sdk.md", "updated_at": "2026-06-18T09:22:23.948Z" } ``` ## Introduction This guide walks you through connecting CrowTerminal to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working CrowTerminal agent that can debug failing docker build command, automate repeated git cleanup commands, recall yesterday's terminal troubleshooting notes through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a CrowTerminal account through Composio's CrowTerminal MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate CrowTerminal with - [OpenAI Agents SDK](https://composio.dev/toolkits/crowterminal/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/crowterminal/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/crowterminal/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/crowterminal/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/crowterminal/framework/codex) - [OpenClaw](https://composio.dev/toolkits/crowterminal/framework/openclaw) - [Hermes](https://composio.dev/toolkits/crowterminal/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/crowterminal/framework/cli) - [Google ADK](https://composio.dev/toolkits/crowterminal/framework/google-adk) - [LangChain](https://composio.dev/toolkits/crowterminal/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/crowterminal/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/crowterminal/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/crowterminal/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with CrowTerminal integration - Using Composio's Tool Router to dynamically load and access CrowTerminal tools - Creating an MCP client connection using HTTP transport - Building an interactive CLI chat interface with conversation history management - Handling tool calls and results within the Vercel AI SDK framework ## What is Vercel AI SDK? The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state. Key features include: - streamText: Core function for streaming responses with real-time tool support - MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp - Step Counting: Control multi-step tool execution with stopWhen: stepCountIs() - OpenAI Provider: Native integration with OpenAI models ## What is the CrowTerminal MCP server, and what's possible with it? The CrowTerminal MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your CrowTerminal account. It provides structured and secure access so your agent can perform CrowTerminal operations on your behalf. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `CROWTERMINAL_ANALYZE_ENGAGEMENT` | Analyze Agent Engagement | Tool to analyze engagement correlation for every field in your agent's markdown. Use when you need to understand which agent configuration fields drive engagement and get specific recommendations for improvement. Returns similarity to best/worst performing versions and field-by-field analysis. | | `CROWTERMINAL_COMPARE_MD` | Compare Agent Markdown | Tool to compare your agent's markdown directly with all stored versions. Returns field differences showing which values differ across versions, lists missing fields not present in your current data, and provides version counts. Use when you need to understand how your current agent configuration compares to historical versions. | | `CROWTERMINAL_CREATE_WEBHOOK` | Create Webhook | Tool to register a new webhook for receiving real-time event notifications from CrowTerminal. Use when you need to set up asynchronous notifications for events like skill updates, data ingestion, or validation blocks. | | `CROWTERMINAL_DELETE_WEBHOOK` | Delete Webhook | Tool to delete an existing webhook registration. Use when you need to remove a webhook that is no longer needed or should be replaced. | | `CROWTERMINAL_GET_BYOK_PLATFORM_INTEL` | Get BYOK Platform Intelligence | Tool to get algorithm insights for TikTok, Instagram, and YouTube without client-specific context. Use when you need platform intelligence data for BYOK (Bring Your Own Key) analysis workflows. This endpoint provides raw contextual algorithm data without triggering LLM inference charges. | | `CROWTERMINAL_GET_CLIENT_MEMORY_CHANGELOG` | Get Client Memory Changelog | Retrieve human-readable change history for a client's memory. Provides a narrative view of how the client's skill data has evolved over time. | | `CROWTERMINAL_GET_CLIENT_MEMORY_PATTERN` | Get Client Memory Pattern | Tool to track a specific field over time for trend analysis. Use when you need to understand how a particular metric evolved across versions or time periods. | | `CROWTERMINAL_GET_COMPONENTS_STATUS` | Get Components Status | Tool to get detailed status of each CrowTerminal service component. Returns current health status, latency, and summary statistics for all monitored components (database, cache, APIs, webhooks). Use when checking system health or diagnosing service issues. | | `CROWTERMINAL_GET_DATA_TYPES` | Get Data Types | Tool to retrieve valid data types for ingestion across platforms. Returns available data types for TikTok, Instagram, and YouTube that can be used for data ingestion operations. | | `CROWTERMINAL_GET_INCIDENTS` | Get Recent Incidents | Tool to retrieve list of recent incidents from CrowTerminal with duration and affected components. Use when you need to check system status, monitor service health, or investigate recent outages or degradations. | | `CROWTERMINAL_GET_PLATFORM_INTEL` | Get Platform Intelligence | Tool to retrieve algorithm insights for TikTok, Instagram, and YouTube. Returns platform-wide intelligence about content algorithm behavior and optimization strategies. Use when you need current platform algorithm trends and recommendations. | | `CROWTERMINAL_GET_SANDBOX_CLIENT` | Get Sandbox Client | Tool to get mock client data for testing in the sandbox environment. Use when you need to test client-related functionality without affecting real data. No authentication required for sandbox endpoints. | | `CROWTERMINAL_GET_SANDBOX_MEMORY` | Get Sandbox Memory | Tool to retrieve mock memory/skill data for testing purposes. Use when you need to test memory retrieval without affecting real data or requiring authentication. Part of the sandbox testing environment. | | `CROWTERMINAL_GET_STATUS` | Get Service Status | Retrieve CrowTerminal service status including overall health, component metrics, and uptime data. Use when you need to check the operational status of CrowTerminal services or monitor system health. No authentication required. | | `CROWTERMINAL_GET_STATUS_HISTORY` | Get Status History | Tool to get 7-day uptime data points ready for visualization and charting. Use when you need historical uptime metrics for monitoring dashboards or status displays. | | `CROWTERMINAL_GET_UPTIME` | Get Uptime Data | Tool to retrieve historical uptime data for CrowTerminal agents. Use when you need to check system reliability, view uptime percentages for 24h/7d periods, or review recent service incidents. | | `CROWTERMINAL_INGEST_BULK_DATA` | Bulk Ingest Analytics Data | Tool to bulk ingest up to 50 analytics data points at once to CrowTerminal. Use when you need to efficiently push large amounts of platform analytics data for content creators across social media platforms. Ideal for batch uploads of retention, engagement, views, and other metrics. | | `CROWTERMINAL_INGEST_DATA` | Ingest Analytics Data | Tool to ingest platform analytics data from TikTok Studio, Instagram Insights, or YouTube Analytics. Use when you need to push retention curves, demographics, traffic sources, or other engagement metrics for analysis. Supports both video-specific and channel-level data ingestion. | | `CROWTERMINAL_LIST_WEBHOOKS` | List Webhooks | Tool to list all registered webhooks for the authenticated agent. Use when you need to view all webhook subscriptions and their configurations. | | `CROWTERMINAL_PING_CROWTERMINAL` | Ping CrowTerminal Service | Tool to check CrowTerminal service availability via a simple ping endpoint. Use when you need to verify the service is online and responding. Returns a pong confirmation with a timestamp. | | `CROWTERMINAL_READ_BULK_MEMORY` | Bulk Read Memory | Tool to read memory for multiple clients at once (up to 50). Use when you need to efficiently retrieve memory data for multiple creators in a single API call. | | `CROWTERMINAL_REGISTER_AGENT` | Register Agent | Tool to self-register a new agent and obtain an API key. Use when you need to create a new agent identity in CrowTerminal. No authentication required for this endpoint. Rate limited to 5 requests per hour per IP address. | | `CROWTERMINAL_RUN_SANDBOX_ENGAGEMENT_ANALYSIS` | Sandbox Engagement Analysis | Tool to run mock engagement analysis in the CrowTerminal sandbox environment. Use when you need to test the engagement analysis workflow without affecting real data or when developing and validating agent configurations. | | `CROWTERMINAL_TEST_WEBHOOK` | Test Webhook | Tool to test a webhook URL by sending a test payload. Use when you need to verify that a webhook endpoint is properly configured and can receive requests. | | `CROWTERMINAL_UPDATE_WEBHOOK` | Update Webhook | Tool to update an existing webhook configuration in CrowTerminal. Use when you need to modify webhook URL, change event subscriptions, or enable/disable a webhook. | | `CROWTERMINAL_VALIDATE_PROPOSED_CHANGES` | Validate Proposed Changes | Tool to validate proposed changes against historical data before updating memory. Use when you need to check if proposed changes contradict historical patterns and receive warnings or recommendations. | | `CROWTERMINAL_VALIDATE_SANDBOX` | Validate Sandbox | Tool to mock validation endpoint for testing in sandbox. Use when you need to test validation logic. Send 'tutorial' in proposedChanges to get a blocked response. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The CrowTerminal MCP server is an implementation of the Model Context Protocol that connects your AI agent to CrowTerminal. It provides structured and secure access so your agent can perform CrowTerminal 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 you begin, make sure you have: - Node.js and npm installed - A Composio account with API key - An OpenAI API key ### 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 required dependencies First, install the necessary packages for your project. What you're installing: - @ai-sdk/openai: Vercel AI SDK's OpenAI provider - @ai-sdk/mcp: MCP client for Vercel AI SDK - @composio/core: Composio SDK for tool integration - ai: Core Vercel AI SDK - dotenv: Environment variable management ```bash npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's needed: - OPENAI_API_KEY: Your OpenAI API key for GPT model access - COMPOSIO_API_KEY: Your Composio API key for tool access - COMPOSIO_USER_ID: A unique identifier for the user session ```bash OPENAI_API_KEY=your_openai_api_key_here COMPOSIO_API_KEY=your_composio_api_key_here COMPOSIO_USER_ID=your_user_id_here ``` ### 4. Import required modules and validate environment What's happening: - We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio - The dotenv/config import automatically loads environment variables - The MCP client import enables connection to Composio's tool server ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Composio } from "@composio/core"; import * as readline from "readline"; import { streamText, type ModelMessage, stepCountIs } from "ai"; import { createMCPClient } from "@ai-sdk/mcp"; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set"); if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set"); const composio = new Composio({ apiKey: composioAPIKey, }); ``` ### 5. Create Tool Router session and initialize MCP client What's happening: - We're creating a Tool Router session that gives your agent access to CrowTerminal tools - The create method takes the user ID and specifies which toolkits should be available - The returned mcp object contains the URL and authentication headers needed to connect to the MCP server - This session provides access to all CrowTerminal-related tools through the MCP protocol ```typescript async function main() { // Create a tool router session for the user const session = await composio.create(composioUserID!, { toolkits: ["crowterminal"], }); const mcpUrl = session.mcp.url; ``` ### 6. Connect to MCP server and retrieve tools What's happening: - We're creating an MCP client that connects to our Composio Tool Router session via HTTP - The mcp.url provides the endpoint, and mcp.headers contains authentication credentials - The type: "http" is important - Composio requires HTTP transport - tools() retrieves all available CrowTerminal tools that the agent can use ```typescript const mcpClient = await createMCPClient({ transport: { type: "http", url: mcpUrl, headers: session.mcp.headers, // Authentication headers for the Composio MCP server }, }); const tools = await mcpClient.tools(); ``` ### 7. Initialize conversation and CLI interface What's happening: - We initialize an empty messages array to maintain conversation history - A readline interface is created to accept user input from the command line - Instructions are displayed to guide the user on how to interact with the agent ```typescript let messages: ModelMessage[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log( "Ask any questions related to crowterminal, like summarize my last 5 emails, send an email, etc... :)))\n", ); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); ``` ### 8. Handle user input and stream responses with real-time tool feedback What's happening: - We use streamText instead of generateText to stream responses in real-time - toolChoice: "auto" allows the model to decide when to use CrowTerminal tools - stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations - onStepFinish callback displays which tools are being used in real-time - We iterate through the text stream to create a typewriter effect as the agent responds - The complete response is added to conversation history to maintain context - Errors are caught and displayed with helpful retry suggestions ```typescript rl.on("line", async (userInput: string) => { const trimmedInput = userInput.trim(); if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } messages.push({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); try { const stream = streamText({ model: openai("gpt-5"), messages, tools, toolChoice: "auto", stopWhen: stepCountIs(10), onStepFinish: (step) => { for (const toolCall of step.toolCalls) { console.log(`[Using tool: ${toolCall.toolName}]`); } if (step.toolCalls.length > 0) { console.log(""); // Add space after tool calls } }, }); for await (const chunk of stream.textStream) { process.stdout.write(chunk); } console.log("\n\n---\n"); // Get final result for message history const response = await stream.response; if (response?.messages?.length) { messages.push(...response.messages); } } catch (error) { console.error("\nAn error occurred while talking to the agent:"); console.error(error); console.log( "\nYou can try again or restart the app if it keeps happening.\n", ); } finally { rl.prompt(); } }); rl.on("close", async () => { await mcpClient.close(); console.log("\n๐Ÿ‘‹ Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Complete Code ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Composio } from "@composio/core"; import * as readline from "readline"; import { streamText, type ModelMessage, stepCountIs } from "ai"; import { createMCPClient } from "@ai-sdk/mcp"; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set"); if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set"); const composio = new Composio({ apiKey: composioAPIKey, }); async function main() { // Create a tool router session for the user const session = await composio.create(composioUserID!, { toolkits: ["crowterminal"], }); const mcpUrl = session.mcp.url; const mcpClient = await createMCPClient({ transport: { type: "http", url: mcpUrl, headers: session.mcp.headers, // Authentication headers for the Composio MCP server }, }); const tools = await mcpClient.tools(); let messages: ModelMessage[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log( "Ask any questions related to crowterminal, like summarize my last 5 emails, send an email, etc... :)))\n", ); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); rl.on("line", async (userInput: string) => { const trimmedInput = userInput.trim(); if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } messages.push({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); try { const stream = streamText({ model: openai("gpt-5"), messages, tools, toolChoice: "auto", stopWhen: stepCountIs(10), onStepFinish: (step) => { for (const toolCall of step.toolCalls) { console.log(`[Using tool: ${toolCall.toolName}]`); } if (step.toolCalls.length > 0) { console.log(""); // Add space after tool calls } }, }); for await (const chunk of stream.textStream) { process.stdout.write(chunk); } console.log("\n\n---\n"); // Get final result for message history const response = await stream.response; if (response?.messages?.length) { messages.push(...response.messages); } } catch (error) { console.error("\nAn error occurred while talking to the agent:"); console.error(error); console.log( "\nYou can try again or restart the app if it keeps happening.\n", ); } finally { rl.prompt(); } }); rl.on("close", async () => { await mcpClient.close(); console.log("\n๐Ÿ‘‹ Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Conclusion You've successfully built a CrowTerminal agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback. Key features of this implementation: - Real-time streaming responses for a better user experience with typewriter effect - Live tool execution feedback showing which tools are being used as the agent works - Dynamic tool loading through Composio's Tool Router with secure authentication - Multi-step tool execution with configurable step limits (up to 10 steps) - Comprehensive error handling for robust agent execution - Conversation history maintenance for context-aware responses You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows. ## How to build CrowTerminal MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/crowterminal/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/crowterminal/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/crowterminal/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/crowterminal/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/crowterminal/framework/codex) - [OpenClaw](https://composio.dev/toolkits/crowterminal/framework/openclaw) - [Hermes](https://composio.dev/toolkits/crowterminal/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/crowterminal/framework/cli) - [Google ADK](https://composio.dev/toolkits/crowterminal/framework/google-adk) - [LangChain](https://composio.dev/toolkits/crowterminal/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/crowterminal/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/crowterminal/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/crowterminal/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 transforms complex browser actions into easy API endpoints for streamlined web interaction. - [Apiflash](https://composio.dev/toolkits/apiflash) - Apiflash is a website screenshot API for programmatically capturing web pages. It delivers high-quality screenshots on demand for automation, monitoring, or reporting. - [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache. - [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence. - [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. 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 CrowTerminal MCP? With a standalone CrowTerminal MCP server, the agents and LLMs can only access a fixed set of CrowTerminal tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from CrowTerminal and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with Vercel AI SDK v6? Yes, you can. Vercel AI SDK v6 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 CrowTerminal tools. ### Can I manage the permissions and scopes for CrowTerminal while using Tool Router? Yes, absolutely. You can configure which CrowTerminal 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 CrowTerminal 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)