# How to integrate Scrape do MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Scrape do MCP with Vercel AI SDK v6", "toolkit": "Scrape do", "toolkit_slug": "scrape_do", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/scrape_do/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/scrape_do/framework/ai-sdk.md", "updated_at": "2026-05-12T10:24:44.960Z" } ``` ## Introduction This guide walks you through connecting Scrape do to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Scrape do agent that can scrape product prices from a dynamic website, extract news headlines with javascript rendering, bypass cloudflare to get full page html through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Scrape do account through Composio's Scrape do MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Scrape do with - [OpenAI Agents SDK](https://composio.dev/toolkits/scrape_do/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/scrape_do/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/scrape_do/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/scrape_do/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/scrape_do/framework/codex) - [OpenClaw](https://composio.dev/toolkits/scrape_do/framework/openclaw) - [Hermes](https://composio.dev/toolkits/scrape_do/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/scrape_do/framework/cli) - [Google ADK](https://composio.dev/toolkits/scrape_do/framework/google-adk) - [LangChain](https://composio.dev/toolkits/scrape_do/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/scrape_do/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/scrape_do/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/scrape_do/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Scrape do integration - Using Composio's Tool Router to dynamically load and access Scrape do 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 Scrape do MCP server, and what's possible with it? The Scrape do MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Scrape do account. It provides structured and secure access to robust web scraping tools, so your agent can perform actions like scraping dynamic pages, managing sessions, setting custom headers or proxies, and extracting structured data from any website on your behalf. - Dynamic page scraping with headless browsers: Retrieve fully rendered HTML content from JavaScript-heavy or protected websites by leveraging advanced browser emulation and proxy rotation. - Custom scraping session management: Set device type, cookies, wait times, and custom headers to imitate different users, maintain sessions, or access device-specific content for tailored data extraction. - Proxy and anti-bot bypass control: Enable super or proxy modes to utilize residential, mobile, or datacenter proxies, helping your agent bypass strict anti-bot systems and geo-restrictions seamlessly. - Targeted resource filtering: Block specific URLs like ads or analytics scripts during scraping to increase speed, avoid distractions, and improve privacy. - Account usage and statistics retrieval: Access real-time usage stats, subscription status, and remaining request limits so your agent can monitor scraping quotas and avoid interruptions. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `SCRAPE_DO_CANCEL_ASYNC_JOB` | Cancel Async Job | Tool to cancel an asynchronous scraping job. Use when you need to stop processing of pending tasks in a job. Completed tasks remain available. | | `SCRAPE_DO_CREATE_ASYNC_JOB` | Create Async Scraping Job | Tool to create an asynchronous scraping job with specified targets and options. Use when you need to scrape multiple URLs in parallel without waiting for results. Returns a job ID immediately for polling results later via the get job status action. | | `SCRAPE_DO_GET_ACCOUNT_INFO` | Get Account Information | Retrieves account information and usage statistics from Scrape.do. This action makes a GET request to the Scrape.do info endpoint to fetch: - Subscription status - Concurrent request limits and usage - Monthly request limits and remaining requests - Real-time usage statistics Rate limit: Maximum 10 requests per minute. Use remaining request counts to monitor credits proactively, as different scraping operations (e.g., rendered-page requests) consume varying credit amounts and exhaustion mid-run causes failures. | | `SCRAPE_DO_GET_AMAZON_OFFERS` | Get Amazon Product Offers | Get all seller offers for any Amazon product. Retrieves every seller listing including pricing, shipping costs, seller information, and Buy Box status in structured JSON format. Use when you need to compare prices across multiple sellers or find the best deal for a specific product. | | `SCRAPE_DO_GET_AMAZON_PRODUCT` | Get Amazon product details | Extract structured product data from Amazon product detail pages (PDP). Returns comprehensive product information including title, pricing, ratings, images, best seller rankings, and technical specifications in JSON format. | | `SCRAPE_DO_GET_AMAZON_RAW_HTML` | Get Amazon raw HTML | Tool to get raw HTML from any Amazon page with ZIP code geo-targeting. Use when you need complete unprocessed HTML source from Amazon URLs with location-based targeting. Ideal for scraping pages not covered by other structured endpoints. | | `SCRAPE_DO_GET_ASYNC_ACCOUNT_INFO` | Get Async API Account Information | Tool to get account information for the Async API including concurrency limits and usage statistics. Use when you need to check available concurrency slots, active jobs, or remaining credits for Async API operations. | | `SCRAPE_DO_GET_ASYNC_JOB` | Get Async Job Details | Tool to retrieve details and status of a specific asynchronous scraping job. Use when you need to check the progress, status, or results of a previously created async job. Returns job metadata including creation time, completion time, task counts, and detailed task list. | | `SCRAPE_DO_GET_ASYNC_TASK` | Get Async Task Result | Tool to retrieve the result of a specific task within an asynchronous job. Returns the scraped content for that particular URL. Use when you need to check the status and result of a previously submitted async scraping task. | | `SCRAPE_DO_SCRAPE_DO_GET_PAGE` | Scrape webpage using scrape.do | A tool to scrape web pages using scrape.do's API service. Makes a basic GET request to fetch webpage content while handling anti-bot protections and proxy rotation automatically. Does not execute JavaScript by default — pages requiring client-side rendering (SPAs, dynamically loaded content) will return incomplete HTML; use SCRAPE_DO_GET_RENDER_PAGE or set render=true for those cases. | | `SCRAPE_DO_LIST_ASYNC_JOBS` | List Asynchronous Scraping Jobs | Tool to list all asynchronous scraping jobs. Returns paginated list of jobs with their status and metadata. Use when you need to retrieve job history or monitor job statuses. Supports pagination with up to 100 jobs per page. | | `SCRAPE_DO_SCRAPE_DO_PROXY_MODE` | Use Scrape.do Proxy Mode | This tool implements the Proxy Mode functionality of scrape.do, which allows routing requests through their proxy server. It provides an alternative way to access web scraping capabilities by handling complex JavaScript-rendered pages, geolocation-based routing, device simulation, and built-in anti-bot and retry mechanisms. | | `SCRAPE_DO_SCRAPE_URL_POST` | Scrape URL using POST method | Tool to scrape web pages using POST method via scrape.do API. Use when you need to send POST requests to target websites with custom request body data. Supports all parameters from GET endpoint plus request body customization for POST/PUT/PATCH methods. | | `SCRAPE_DO_SEARCH_AMAZON` | Search Amazon products | Tool to search Amazon and scrape product listings with structured results. Performs keyword searches and returns structured product data including titles, prices, ratings, Prime status, sponsored flags, and position rankings in JSON format. Use when you need to search for products on Amazon marketplace or gather product information from search results. | | `SCRAPE_DO_SET_BLOCK_URLS` | Block specific URLs during scraping | This tool allows users to block specific URLs during the scraping process. It's particularly useful for blocking unwanted resources like analytics scripts, advertisements, or any other URLs that might interfere with the scraping process or slow it down. It provides granular control by allowing users to specify URL patterns to block, thereby improving scraping performance and maintaining privacy. | | `SCRAPE_DO_SET_REGIONAL_GEO_CODE` | Set Regional Geolocation for Scraping | This tool allows users to set a broader geographical targeting by specifying a region code instead of a specific country code. This is useful when you want to scrape content from an entire region rather than a specific country. Note that this feature requires super mode to be enabled and is only available for Business Plan or higher subscriptions. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Scrape do MCP server is an implementation of the Model Context Protocol that connects your AI agent to Scrape do. It provides structured and secure access so your agent can perform Scrape do 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 Scrape do 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 Scrape do-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: ["scrape_do"], }); 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 Scrape do 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 scrape_do, 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 Scrape do 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("\n👋 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: ["scrape_do"], }); 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 scrape_do, 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("\n👋 Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Conclusion You've successfully built a Scrape do 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 Scrape do MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/scrape_do/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/scrape_do/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/scrape_do/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/scrape_do/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/scrape_do/framework/codex) - [OpenClaw](https://composio.dev/toolkits/scrape_do/framework/openclaw) - [Hermes](https://composio.dev/toolkits/scrape_do/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/scrape_do/framework/cli) - [Google ADK](https://composio.dev/toolkits/scrape_do/framework/google-adk) - [LangChain](https://composio.dev/toolkits/scrape_do/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/scrape_do/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/scrape_do/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/scrape_do/framework/crew-ai) ## Related Toolkits - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. It's the go-to tool for calculations, reporting, and flexible data management. - [21risk](https://composio.dev/toolkits/_21risk) - 21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters. - [Abstract](https://composio.dev/toolkits/abstract) - Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort. - [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time. - [Agenty](https://composio.dev/toolkits/agenty) - Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. It streamlines data collection, monitoring, and repetitive online actions. - [Ambee](https://composio.dev/toolkits/ambee) - Ambee is an environmental data platform providing real-time, hyperlocal APIs for air quality, weather, and pollen. Get precise environmental insights to power smarter decisions in your apps and workflows. - [Ambient weather](https://composio.dev/toolkits/ambient_weather) - Ambient Weather is a platform for personal weather stations with a robust API for accessing local, real-time, and historical weather data. Get detailed environmental insights directly from your own sensors for smarter apps and automations. - [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements. - [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints. - [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots. - [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches. - [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand. - [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance. - [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows. - [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses. - [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research. - [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction. - [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation. - [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation. - [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Scrape do MCP? With a standalone Scrape do MCP server, the agents and LLMs can only access a fixed set of Scrape do tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Scrape do 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 Scrape do tools. ### Can I manage the permissions and scopes for Scrape do while using Tool Router? Yes, absolutely. You can configure which Scrape do 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 Scrape do data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)