# How to integrate Scrapegraph ai MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Scrapegraph ai MCP with Vercel AI SDK v6", "toolkit": "Scrapegraph ai", "toolkit_slug": "scrapegraph_ai", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/scrapegraph_ai/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/scrapegraph_ai/framework/ai-sdk.md", "updated_at": "2026-05-12T10:24:47.118Z" } ``` ## Introduction This guide walks you through connecting Scrapegraph ai to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Scrapegraph ai agent that can extract product prices from amazon search results, summarize latest news headlines from bbc homepage, convert wikipedia article to markdown format through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Scrapegraph ai account through Composio's Scrapegraph ai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Scrapegraph ai with - [OpenAI Agents SDK](https://composio.dev/toolkits/scrapegraph_ai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/scrapegraph_ai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/scrapegraph_ai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/scrapegraph_ai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/scrapegraph_ai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/scrapegraph_ai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/scrapegraph_ai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/scrapegraph_ai/framework/cli) - [Google ADK](https://composio.dev/toolkits/scrapegraph_ai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/scrapegraph_ai/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/scrapegraph_ai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/scrapegraph_ai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/scrapegraph_ai/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Scrapegraph ai integration - Using Composio's Tool Router to dynamically load and access Scrapegraph ai 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 Scrapegraph ai MCP server, and what's possible with it? The Scrapegraph ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Scrapegraph ai account. It provides structured and secure access to powerful web scraping and data extraction tools, so your agent can perform actions like running AI-powered scrapers, converting webpages to markdown, monitoring job statuses, and managing your account usage with ease. - AI-powered web scraping and search: Instruct your agent to extract structured data from any website or perform detailed web searches with parsed, organized results. - Webpage to markdown conversion: Let your agent instantly convert any webpage into clean, readable markdown for easy documentation or analysis. - Automated job status tracking: Check on the progress and results of ongoing scraping, crawling, or conversion jobs to stay updated without manual effort. - Smart multi-page crawling: Direct the agent to launch intelligent crawlers that gather data across multiple linked pages in a single workflow. - Account usage monitoring and feedback: Retrieve your remaining credits, track API usage, and submit feedback on completed tasks—all through your AI agent. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `SCRAPEGRAPH_AI_CONVERT_WEBPAGE_TO_MARKDOWN_V2` | Convert Webpage to Markdown (V2) | Tool to convert any webpage into clean, well-formatted Markdown with full parameter control. Use when you need advanced options like stealth mode, custom headers, or webhook notifications. Supports all Markdownify API parameters. | | `SCRAPEGRAPH_AI_GENERATE_SCHEMA` | Generate Schema | Generate or modify a JSON schema based on a search query for structured data extraction. Use when you need a schema template for scraping specific data fields. | | `SCRAPEGRAPH_AI_GET_AGENTIC_SCRAPER_HISTORY` | Get Agentic Scraper History | Retrieve paginated history of agentic scraper jobs. Use to view past scraping requests, their status, and results. | | `SCRAPEGRAPH_AI_GET_CRAWLER_HISTORY` | Get Crawler History | Retrieve the history of crawler jobs for your account. Returns paginated list of past crawler requests with their status, results, and metadata. | | `SCRAPEGRAPH_AI_GET_CREDITS` | Get Credits | Retrieve remaining and used credits for your ScrapeGraphAI account. Useful for checking credit availability before bulk scraping operations to avoid mid-run failures. | | `SCRAPEGRAPH_AI_GET_ENDPOINT_SUGGESTIONS` | Get Endpoint Suggestions | Tool to get AI-powered suggestions for creating scraping endpoints. Use when you need to identify what data can be extracted from a website and how to structure the scraping logic. | | `SCRAPEGRAPH_AI_GET_LIVE_SESSION_URL` | Get Live Session URL | Tool to get a URL for a live browser session. Use when you need to interact with a webpage in real-time through a controlled browser environment. | | `SCRAPEGRAPH_AI_GET_MARKDOWNIFY_HISTORY` | Get Markdownify History | Tool to retrieve the history of markdownify webpage-to-Markdown conversion jobs. Use when you need to view past markdownify requests and their statuses. | | `SCRAPEGRAPH_AI_GET_SCRAPE_HISTORY` | Get Scrape History | Retrieve the history of scrape jobs from your ScrapeGraphAI account. Use this to check the status of past scrapes, view results, and track credit usage. | | `SCRAPEGRAPH_AI_GET_SEARCHSCRAPER_HISTORY` | Get Searchscraper History | Get the history of searchscraper jobs with pagination support. Use this to retrieve past searchscraper requests, their status, and results. | | `SCRAPEGRAPH_AI_GET_SITEMAP_HISTORY` | Get Sitemap History | Tool to retrieve the history of sitemap extraction jobs. Use when you need to view past sitemap extraction requests, their status, and results. | | `SCRAPEGRAPH_AI_GET_SMARTSCRAPER_HISTORY` | Get Smartscraper History | Tool to retrieve the history of smartscraper jobs. Use when you need to view past scraping requests and their results. | | `SCRAPEGRAPH_AI_GET_USAGE_TIMELINE` | Get Usage Timeline | Tool to retrieve usage timeline statistics for your ScrapeGraphAI account. Use when you need to visualize or analyze service usage patterns over time. | | `SCRAPEGRAPH_AI_GET_WEBHOOK_LOGS` | Get Webhook Logs | Tool to retrieve webhook delivery logs for a crawler job. Use when you need to check the status and history of webhook notifications sent for a specific crawler execution. | | `SCRAPEGRAPH_AI_LIST_SCHEDULED_JOBS` | List Scheduled Jobs | Retrieve a paginated list of all scheduled scraping jobs for your account. Use this action to view and manage your scheduled jobs, including their configuration, cron schedules, and active status. Supports filtering by service type and active status. | | `SCRAPEGRAPH_AI_MARKDOWNIFY_STATUS` | Markdownify Status | Check the status and retrieve results of a Markdownify webpage-to-Markdown conversion job. Use this action to poll for the status of an async Markdownify request started via SCRAPEGRAPH_AI_MARKDOWNIFY. Note: The ScrapeGraph AI API typically returns completed results synchronously, so this status endpoint is primarily useful for long-running conversions of large or complex webpages. | | `SCRAPEGRAPH_AI_SAVE_ENDPOINT` | Save Endpoint Configuration | Tool to save custom scraping endpoint configurations to ScrapeGraphAI. Use when you need to create reusable scraping endpoints with specific parameters and extraction logic. | | `SCRAPEGRAPH_AI_SEARCH_SCRAPER` | Search Scraper | Perform AI-powered web searches with structured, parsed results. Some sites block scrapers and return empty bodies; treat these as unrecoverable for that URL. JS-rendered pages may yield incomplete content. | | `SCRAPEGRAPH_AI_SEARCH_SCRAPER_STATUS` | Check SearchScraper Status | Check the status and results of an asynchronous SearchScraper job. | | `SCRAPEGRAPH_AI_SMART_CRAWLER_STATUS` | SmartCrawler Status | Check the status and retrieve results of a SmartCrawler web crawling job. Use this action to poll for completion and get the extracted content from a previously started SmartCrawler job. Returns the job status, crawled URLs, page content in markdown/HTML format, and LLM extraction results (if enabled). Implement a polling timeout (e.g., max retries or elapsed time cap) to avoid indefinite loops when waiting for long-running jobs. | | `SCRAPEGRAPH_AI_SMART_SCRAPER_START` | Start Smart Scraper | Start AI-powered web scraping with natural language extraction prompts. When `wait` is false (default), returns a `request_id`; poll for results using SCRAPEGRAPH_AI_SMART_SCRAPER_STATUS. Check `error` and `job_status` fields in the response before using extracted data. | | `SCRAPEGRAPH_AI_SMART_SCRAPER_STATUS` | SmartScraper Status | Check the status and retrieve results of a SmartScraper web scraping job. Use this action to poll for completion after starting a SmartScraper job with wait=false. The request_id is returned by the Start SmartScraper action. Typical workflow: 1. Start a scraping job with SCRAPEGRAPH_AI_SMART_SCRAPER_START (wait=false) 2. Use the returned request_id to check status with this action 3. Poll until status is 'completed' or 'failed' 4. When completed, the 'result' field contains the extracted data. When completed, also check the 'error' field before consuming 'result', as 'failed' status populates 'error' instead of 'result'. | | `SCRAPEGRAPH_AI_START_SMART_CRAWLER` | Start Smart Crawler (Async) | Tool to start a multi-page web crawl using SmartCrawler for AI-powered data extraction. Use when you need to extract structured data from multiple pages of a website. Returns immediately with a task_id - use the status check action to monitor progress and retrieve results. | | `SCRAPEGRAPH_AI_SUBMIT_FEEDBACK` | Submit Feedback | Submit feedback and ratings for completed ScrapeGraphAI requests. | | `SCRAPEGRAPH_AI_SUBMIT_PRODUCT_FEEDBACK` | Submit Product Feedback | Submit product feedback for ScrapeGraphAI. Use to provide ratings, comments, suggestions, and other feedback about the product itself. | | `SCRAPEGRAPH_AI_TOONIFY` | Convert JSON to TOON Format | Tool to convert JSON data to TOON (Token-Oriented Object Notation) format. Use when you need to reduce token usage for LLM processing while maintaining data structure. | | `SCRAPEGRAPH_AI_VALIDATE_API_KEY` | Validate API Key | Validate your ScrapeGraphAI API key to ensure it is active and authorized. Use this action to check API key validity before making other API calls. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Scrapegraph ai MCP server is an implementation of the Model Context Protocol that connects your AI agent to Scrapegraph ai. It provides structured and secure access so your agent can perform Scrapegraph ai 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 Scrapegraph ai 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 Scrapegraph ai-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: ["scrapegraph_ai"], }); 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 Scrapegraph ai 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 scrapegraph_ai, 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 Scrapegraph ai 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: ["scrapegraph_ai"], }); 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 scrapegraph_ai, 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 Scrapegraph ai 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 Scrapegraph ai MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/scrapegraph_ai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/scrapegraph_ai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/scrapegraph_ai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/scrapegraph_ai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/scrapegraph_ai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/scrapegraph_ai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/scrapegraph_ai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/scrapegraph_ai/framework/cli) - [Google ADK](https://composio.dev/toolkits/scrapegraph_ai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/scrapegraph_ai/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/scrapegraph_ai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/scrapegraph_ai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/scrapegraph_ai/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 Scrapegraph ai MCP? With a standalone Scrapegraph ai MCP server, the agents and LLMs can only access a fixed set of Scrapegraph ai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Scrapegraph ai 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 Scrapegraph ai tools. ### Can I manage the permissions and scopes for Scrapegraph ai while using Tool Router? Yes, absolutely. You can configure which Scrapegraph ai 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 Scrapegraph ai data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)