# How to integrate Codereadr MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Codereadr MCP with Vercel AI SDK v6", "toolkit": "Codereadr", "toolkit_slug": "codereadr", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/codereadr/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/codereadr/framework/ai-sdk.md", "updated_at": "2026-05-12T10:07:02.660Z" } ``` ## Introduction This guide walks you through connecting Codereadr to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Codereadr agent that can create a new barcode scanning service, configure survey questions after each scan, enable kiosk mode for unattended device through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Codereadr account through Composio's Codereadr MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Codereadr with - [OpenAI Agents SDK](https://composio.dev/toolkits/codereadr/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/codereadr/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/codereadr/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/codereadr/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/codereadr/framework/codex) - [OpenClaw](https://composio.dev/toolkits/codereadr/framework/openclaw) - [Hermes](https://composio.dev/toolkits/codereadr/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/codereadr/framework/cli) - [Google ADK](https://composio.dev/toolkits/codereadr/framework/google-adk) - [LangChain](https://composio.dev/toolkits/codereadr/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/codereadr/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/codereadr/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/codereadr/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Codereadr integration - Using Composio's Tool Router to dynamically load and access Codereadr 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 Codereadr MCP server, and what's possible with it? The Codereadr MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Codereadr account. It provides structured and secure access to your data collection and barcode scanning workflows, so your agent can create services, configure scan workflows, manage databases, and automate data collection processes for you. - Automated service and workflow setup: Let your agent create new CodeREADr services and configure custom workflows for scanning, picking, delivery, and receiving tasks. - Custom data collection form creation: Easily set up or modify data capture forms by adding or deleting custom questions after each scan. - Real-time scan integration: Configure Direct Scan URLs, postback endpoints, or Google Sheets connectors to forward scan results instantly to your desired platforms. - Device and database management: Direct your agent to delete devices or entire databases when they are no longer needed, streamlining your data environment. - Kiosk and unattended scanning configuration: Enable and fine-tune Kiosk Mode for unattended or dedicated scanning stations to support high-volume operations. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `CODEREADR_COLLECT_DATA_WITH_QUESTIONS` | Collect Data With Questions | Create and attach custom questions to a CodeREADr service for data collection after scans. Use this to configure forms that collect additional information from users after each barcode scan. Requires a valid service ID from CODEREADR_RETRIEVE_SERVICES or CODEREADR_CREATE_SERVICE. | | `CODEREADR_CONFIGURE_CONNECTOR` | Configure CodeREADr Connector | Helper to guide configuring the CodeREADr Connector for Google Sheets. There is no public API to programmatically create connector configurations. This tool validates your API connectivity (optional) and returns clear steps to proceed via the Google Sheets Add-on UI: https://www.codereadr.com/knowledgebase/codereadr-connector-add-on/ | | `CODEREADR_CREATE_SERVICE` | Create CodeREADr Service | Creates a new CodeREADr service (barcode scanning workflow configuration). A service defines how barcode scans are processed - whether they're simply recorded, validated against a database, forwarded to an external URL, or display web content. Each validation_method type has different required parameters: 'database'/'ondevicedatabase' require database_id, 'postback' requires postback_url, 'webview' requires description (URL/HTML). | | `CODEREADR_DELETE_DATABASE` | Delete CodeREADr Database | Delete a CodeREADr validation database by its ID. This permanently removes the database and all its barcode values. Use with caution. Note: A database cannot be deleted if it is currently linked to one or more services. You must unlink those services from the database first. Example: "Delete database with ID 1340798" | | `CODEREADR_DELETE_DEVICE` | Delete Device | Tool to delete a device from CodeREADr. Uses the CodeREADr legacy API with section=devices and action=delete parameters. Note: Device deletion may have limited support in the CodeREADr API - only 'retrieve' and 'update' actions are officially documented for devices. | | `CODEREADR_DELETE_QUESTION` | Delete Custom Question | Permanently deletes one or more custom questions from your CodeREADr account. Questions are used to collect additional data after scans. Once deleted, the question and all associated answer options are removed. This action cannot be undone. | | `CODEREADR_DELETE_SERVICE` | Delete CodeREADr Service | Delete a CodeREADr service by its numeric ID. Use this to permanently remove a service/workflow configuration from your account. Note: This is a destructive action and cannot be undone. You can delete a single service, multiple services (comma-separated IDs), or all services. Example: "Delete service with ID 12345" | | `CODEREADR_DELETE_USER` | Delete CodeREADr User | Deletes an existing user account from CodeREADr. Uses the CodeREADr legacy API endpoint (POST /api/ with section=users, action=delete). The user_id parameter can be a single ID, comma-separated list of IDs, or 'all'. Note: You cannot delete the account owner's app-user. The API will return an error if an invalid user_id is provided. | | `CODEREADR_GENERATE_SCAN_LINK` | Generate Scan Link | Generates a CodeREADr scan link URI that opens the CodeREADr mobile app with a pre-filled scan value. Use this tool when you need to create clickable links that launch the CodeREADr scanner with a specific barcode, QR code, or identifier already entered. | | `CODEREADR_LIST_SUPPORTED_BARCODE_TYPES` | List Supported Barcode Types | Lists barcode symbologies supported by CodeREADr for scanning. Returns 1D barcodes (Code 39, Code 128, EAN, UPC, Codabar, etc.), 2D barcodes (QR Code, Data Matrix, PDF-417, Aztec, etc.), and specialized formats. Use this to verify if a specific barcode type is supported before scanning. | | `CODEREADR_RETRIEVE_DATABASES` | Retrieve CodeREADr Databases | Retrieves all validation databases configured in your CodeREADr account. Use this to list databases for barcode validation, see their IDs, names, item counts, and which services they're linked to. | | `CODEREADR_RETRIEVE_DEVICES` | Retrieve Devices | Retrieve a list of devices registered to your CodeREADr account. This tool fetches information about devices linked to your account, including device IDs, UDIDs, names, and creation timestamps. Use this to monitor which devices have access to your CodeREADr services. | | `CODEREADR_RETRIEVE_SCANS` | Retrieve Scan Records | Retrieve scan records from your CodeREADr account. Scans are the core data collected by CodeREADr when users scan barcodes using the mobile app. Each scan record includes the barcode value, timestamp, device info, validation status, and any collected responses. Use filters to narrow down results by service, user, device, date range, or status. Returns scan records in batches. Use limit and offset parameters for pagination. | | `CODEREADR_RETRIEVE_SERVICES` | Retrieve CodeREADr Services | Retrieve configured services from your CodeREADr account. Services are the core organizational units in CodeREADr that define how barcode scans are validated and processed. Use this action to list all services or retrieve specific services by ID. | | `CODEREADR_UPDATE_QUESTION` | Update CodeREADr Question | Add answer options to an existing CodeREADr question. Use this to add selectable answers for checkbox, dropdown, or option-type questions. The CodeREADr API does not support updating question text - to change text, delete and recreate the question. | | `CODEREADR_UPDATE_SERVICE` | Update CodeREADr Service | Update an existing CodeREADr service configuration. Use this action to modify settings of a service by its ID. Only specified fields will be updated - omitted fields retain their current values. Common use cases: - Renaming a service - Changing postback/webhook URL - Enabling/disabling GPS tracking - Modifying duplicate scan handling - Setting time restrictions for service availability | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Codereadr MCP server is an implementation of the Model Context Protocol that connects your AI agent to Codereadr. It provides structured and secure access so your agent can perform Codereadr 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 Codereadr 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 Codereadr-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: ["codereadr"], }); 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 Codereadr 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 codereadr, 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 Codereadr 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: ["codereadr"], }); 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 codereadr, 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 Codereadr 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 Codereadr MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/codereadr/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/codereadr/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/codereadr/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/codereadr/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/codereadr/framework/codex) - [OpenClaw](https://composio.dev/toolkits/codereadr/framework/openclaw) - [Hermes](https://composio.dev/toolkits/codereadr/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/codereadr/framework/cli) - [Google ADK](https://composio.dev/toolkits/codereadr/framework/google-adk) - [LangChain](https://composio.dev/toolkits/codereadr/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/codereadr/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/codereadr/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/codereadr/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. - [Agentql](https://composio.dev/toolkits/agentql) - Agentql is a toolkit that connects AI agents to the web using a specialized query language. It enables structured web interaction and data extraction for smarter automations. - [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. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Codereadr MCP? With a standalone Codereadr MCP server, the agents and LLMs can only access a fixed set of Codereadr tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Codereadr 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 Codereadr tools. ### Can I manage the permissions and scopes for Codereadr while using Tool Router? Yes, absolutely. You can configure which Codereadr 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 Codereadr data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)