# How to integrate Callingly MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Callingly MCP with Vercel AI SDK v6", "toolkit": "Callingly", "toolkit_slug": "callingly", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/callingly/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/callingly/framework/ai-sdk.md", "updated_at": "2026-05-12T10:04:52.223Z" } ``` ## Introduction This guide walks you through connecting Callingly to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Callingly agent that can activate a client account for onboarding, create an outbound call to new lead, get agent schedule for next week through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Callingly account through Composio's Callingly MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Callingly with - [OpenAI Agents SDK](https://composio.dev/toolkits/callingly/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/callingly/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/callingly/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/callingly/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/callingly/framework/codex) - [OpenClaw](https://composio.dev/toolkits/callingly/framework/openclaw) - [Hermes](https://composio.dev/toolkits/callingly/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/callingly/framework/cli) - [Google ADK](https://composio.dev/toolkits/callingly/framework/google-adk) - [LangChain](https://composio.dev/toolkits/callingly/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/callingly/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/callingly/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/callingly/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Callingly integration - Using Composio's Tool Router to dynamically load and access Callingly 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 Callingly MCP server, and what's possible with it? The Callingly MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Callingly account. It provides structured and secure access to your Callingly environment, so your agent can automate lead follow-ups, manage agents and clients, trigger outbound calls, and keep your sales workflows moving without manual intervention. - Automated outbound call creation: Instantly generate outbound call records so your team can respond to new leads within seconds without lifting a finger. - Agent and team management: Let your agent create, delete, or update agents and teams as your sales organization changes and grows. - Client onboarding and offboarding: Seamlessly add, activate, deactivate, or remove client accounts as your business requires—no more manual data entry. - Real-time webhook setup: Set up and delete webhooks to receive instant notifications for specific call or lead events, keeping your CRM and other tools in sync. - Availability and scheduling insights: Retrieve agent schedules to optimize call assignments and guarantee leads get connected when agents are actually available. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `CALLINGLY_ACTIVATE_DEACTIVATE_CLIENT` | Activate/Deactivate Client Account | Tool to activate or deactivate a client account. Use when you need to toggle client access after onboarding or offboarding. Example: "Activate client 123" or "Deactivate client 456". | | `CALLINGLY_CREATE_AGENT` | Create Agent | Tool to create a new agent. Use when you need to register an agent in Callingly after gathering their account ID and contact details. | | `CALLINGLY_CREATE_CALL` | Create Outbound Call | Creates a new outbound call record and initiates a real outbound call, which incurs cost — ensure explicit user authorization and compliance with applicable consent and telemarketing regulations before use. The call will be routed to available agents on the specified team based on account-level routing configuration. Use List Teams first to get valid account_id and team_id values. Returns a call_id that can be used with Get Call to retrieve call status, recordings, and other details. | | `CALLINGLY_CREATE_CLIENT` | Create Client | Creates a new client account in Callingly. Clients are sub-accounts under your agency account that can have their own teams, agents, and billing. Use this when onboarding a new business customer to your Callingly agency. | | `CALLINGLY_CREATE_TEAM` | Create Team | Tool to create a new team. Use when setting up a team configuration before adding agents. | | `CALLINGLY_CREATE_WEBHOOK` | Create Webhook | Tool to create a new webhook for call or lead events. Use when you need to receive real-time notifications on specific events. Example: "Create a webhook for call_completed events to http://example.com/callback". | | `CALLINGLY_DELETE_AGENT` | Delete Agent | Permanently delete an agent from a Callingly account. Use when removing an agent who should no longer receive calls. Requires both the agent ID and the account ID the agent belongs to. | | `CALLINGLY_DELETE_CLIENT` | Delete Client | Tool to delete a client. Use when you need to remove an existing client from your account after confirming its ID. | | `CALLINGLY_DELETE_LEAD` | Delete Lead | Tool to delete a lead by ID. Use when you need to permanently remove a lead from your account after confirming its ID. Returns a success confirmation. | | `CALLINGLY_DELETE_WEBHOOK` | Delete Webhook | Tool to delete a webhook. Use when permanently removing a webhook by its ID. | | `CALLINGLY_GET_AGENT_SCHEDULE` | Get Agent Schedule | Tool to retrieve the availability schedule for a specific agent. Use when you need to know which days and times the agent is available. | | `CALLINGLY_GET_CALL` | Get Call | Retrieves detailed information about a specific call by its unique ID. Returns comprehensive call metadata including status, duration, lead information, agent details, recording URLs, transcripts, and AI-generated insights. Use the LIST_CALLS action first to obtain valid call IDs. | | `CALLINGLY_GET_LEAD` | Get Lead | Tool to retrieve details of a specific lead by its ID. Use when you need full lead details before follow-up actions. | | `CALLINGLY_GET_TEAM` | Get Team | Tool to retrieve details of a specific team. Use after obtaining the team ID to fetch its configuration details. | | `CALLINGLY_GET_WEBHOOK` | Get Webhook | Tool to retrieve details of a specific webhook by its ID. Use when you need to inspect a webhook's configuration before modifying or deleting it. | | `CALLINGLY_LIST_CALLS` | List Calls | Tool to list calls. Use when you need to retrieve multiple call records with optional filters such as date range, team, and pagination after identifying the need for a collection of calls. | | `CALLINGLY_LIST_CLIENTS` | List Clients | Tool to list clients. Use when you need to retrieve all clients associated with your account. | | `CALLINGLY_LIST_LEADS` | List Leads | Tool to list leads based on provided filters like date range or phone number. Use after confirming filter criteria when bulk lead retrieval is needed. | | `CALLINGLY_LIST_TEAMS` | List Teams | Tool to list teams. Use when you need to retrieve all teams associated with your account. | | `CALLINGLY_LIST_TEAM_USERS` | List Team Users | Retrieve all agents assigned to a specific team in Callingly. Returns each agent's ID, name, priority, call cap, and any custom/integration identifiers. Use this to audit team composition or before performing agent management operations like updates or removals. | | `CALLINGLY_LIST_USERS` | List Users | Tool to retrieve a list of agents. Use when you need to see all agents available under the authenticated account, optionally filtering by a specific client account. | | `CALLINGLY_LIST_WEBHOOKS` | List Webhooks | Tool to list configured webhooks. Use when you need to retrieve all webhooks configured in your account to review or manage them. | | `CALLINGLY_REMOVE_TEAM_AGENT` | Remove Team Agent | Tool to remove a specific agent from a team. Use when you need to disassociate an agent from a team after confirming both team and agent IDs. | | `CALLINGLY_UPDATE_AGENT` | Update Agent | Tool to update an existing agent's details. Use when you need to modify agent information post-creation. | | `CALLINGLY_UPDATE_LEAD` | Update Lead | Tool to update an existing lead's information. Use when you need to modify lead contact details, status, or blocking settings. | | `CALLINGLY_UPDATE_SCHEDULE` | Update Agent Schedule | Tool to update an agent's availability schedule. Use when you need to set or override an agent's daily availability times. | | `CALLINGLY_UPDATE_TEAM_AGENT_SETTINGS` | Update Team Agent Settings | Tool to update settings (priority, capacity) for a specific team agent. Use when adjusting an agent's priority or call capacity after team configuration. | | `CALLINGLY_UPDATE_TEAM_USERS` | Update Team Users | Updates the list of agents assigned to a team. This operation replaces all existing agent assignments - any agents not included in the list will be removed from the team. Use CALLINGLY_LIST_TEAM_USERS to check current assignments before updating. | | `CALLINGLY_UPDATE_WEBHOOK` | Update Webhook | Updates an existing webhook's configuration by ID. Supports partial updates - only provide fields you want to change. Note: When updating event-specific fields (call_status, call_lead_status, field, filter), you must also include the event field in your request. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Callingly MCP server is an implementation of the Model Context Protocol that connects your AI agent to Callingly. It provides structured and secure access so your agent can perform Callingly 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 Callingly 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 Callingly-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: ["callingly"], }); 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 Callingly 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 callingly, 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 Callingly 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: ["callingly"], }); 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 callingly, 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 Callingly 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 Callingly MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/callingly/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/callingly/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/callingly/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/callingly/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/callingly/framework/codex) - [OpenClaw](https://composio.dev/toolkits/callingly/framework/openclaw) - [Hermes](https://composio.dev/toolkits/callingly/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/callingly/framework/cli) - [Google ADK](https://composio.dev/toolkits/callingly/framework/google-adk) - [LangChain](https://composio.dev/toolkits/callingly/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/callingly/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/callingly/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/callingly/framework/crew-ai) ## Related Toolkits - [Aeroleads](https://composio.dev/toolkits/aeroleads) - Aeroleads is a B2B lead generation platform for finding business emails and phone numbers. Grow your sales pipeline faster with powerful prospecting tools. - [Autobound](https://composio.dev/toolkits/autobound) - Autobound is an AI-powered sales engagement platform that crafts hyper-personalized outreach and insights. It helps sales teams boost response rates and close more deals through tailored content and recommendations. - [Better proposals](https://composio.dev/toolkits/better_proposals) - Better Proposals is a web-based tool for crafting and sending professional proposals. It helps teams impress clients and close deals faster with slick, easy-to-use templates. - [Bidsketch](https://composio.dev/toolkits/bidsketch) - Bidsketch is a proposal software that helps businesses create professional proposals quickly and efficiently. It streamlines the proposal process, saving time while boosting client win rates. - [Bolna](https://composio.dev/toolkits/bolna) - Bolna is an AI platform for building conversational voice agents. It helps businesses automate support and streamline interactions through natural, voice-powered conversations. - [Botsonic](https://composio.dev/toolkits/botsonic) - Botsonic is a no-code AI chatbot builder for easily creating and deploying chatbots to your website. It empowers businesses to offer conversational experiences without writing code. - [Botstar](https://composio.dev/toolkits/botstar) - BotStar is a comprehensive chatbot platform for designing, developing, and training chatbots visually on Messenger and websites. It helps businesses automate conversations and customer interactions without coding. - [Callerapi](https://composio.dev/toolkits/callerapi) - CallerAPI is a white-label caller identification platform for branded caller ID and fraud prevention. It helps businesses boost customer trust while stopping spam, fraud, and robocalls. - [Callpage](https://composio.dev/toolkits/callpage) - Callpage is a lead capture platform that lets businesses instantly connect with website visitors via callback. It boosts lead generation and increases your sales conversion rates. - [Clearout](https://composio.dev/toolkits/clearout) - Clearout is an AI-powered service for verifying, finding, and enriching email addresses. It boosts deliverability and helps you discover high-quality leads effortlessly. - [Clientary](https://composio.dev/toolkits/clientary) - Clientary is a platform for managing clients, invoices, projects, proposals, and more. It streamlines client work and saves you serious admin time. - [Convolo ai](https://composio.dev/toolkits/convolo_ai) - Convolo ai is an AI-powered communications platform for sales teams. It accelerates lead response and improves conversion rates by automating calls and integrating workflows. - [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment. - [Docsbot ai](https://composio.dev/toolkits/docsbot_ai) - Docsbot ai is a platform that lets you build custom AI chatbots trained on your documentation. It automates customer support and content generation, saving time and improving response quality. - [Emelia](https://composio.dev/toolkits/emelia) - Emelia is an all-in-one B2B prospecting platform for cold-email, LinkedIn outreach, and prospect research. It streamlines outbound campaigns so you can find, engage, and warm up leads faster. - [Findymail](https://composio.dev/toolkits/findymail) - Findymail is a B2B data provider offering verified email and phone contacts for sales prospecting. Enhance outreach with automated exports, email verification, and CRM enrichment. - [Freshdesk](https://composio.dev/toolkits/freshdesk) - Freshdesk is customer support software with ticketing and automation tools. It helps teams streamline helpdesk operations for faster, better customer support. - [Fullenrich](https://composio.dev/toolkits/fullenrich) - FullEnrich is a B2B contact enrichment platform that aggregates emails and phone numbers from 15+ data vendors. Instantly find and verify lead contact data to boost your outreach. - [Gatherup](https://composio.dev/toolkits/gatherup) - GatherUp is a customer feedback and online review management platform. It helps businesses boost their reputation by streamlining how they collect and manage customer feedback. - [Getprospect](https://composio.dev/toolkits/getprospect) - Getprospect is a business email discovery tool with LinkedIn integration. Use it to quickly find and verify professional email addresses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Callingly MCP? With a standalone Callingly MCP server, the agents and LLMs can only access a fixed set of Callingly tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Callingly 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 Callingly tools. ### Can I manage the permissions and scopes for Callingly while using Tool Router? Yes, absolutely. You can configure which Callingly 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 Callingly data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)