How to integrate Retellai MCP with Mastra AI

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Introduction

This guide walks you through connecting Retellai to Mastra AI using the Composio tool router. By the end, you'll have a working Retellai agent that can list all phone numbers linked to your account, retrieve call details for a specific agent this week, buy a new phone number with area code 415 through natural language commands.

This guide will help you understand how to give your Mastra AI agent real control over a Retellai account through Composio's Retellai MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Retellai with

TL;DR

Here's what you'll learn:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes Retellai tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Retellai tool definitions and attach them as a toolset
  • Build a Mastra agent that can reason, call tools, and return structured results
  • Run an interactive CLI where you can chat with your Retellai agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

  • MCP Client: Built-in support for Model Context Protocol servers
  • Toolsets: Organize tools into logical groups
  • Step Callbacks: Monitor and debug agent execution
  • OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

What is the Retellai MCP server, and what's possible with it?

The Retellai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Retellai account. It provides structured and secure access to your call records, phone numbers, and conversation transcripts, so your agent can perform actions like retrieving call details, managing phone numbers, initiating outbound calls, and analyzing voice data on your behalf.

  • Retrieve and analyze call records: Your agent can fetch detailed call logs, filter by agent or time, and surface insights from past conversations.
  • Initiate outbound and web-based calls: Easily direct your agent to start new phone or web calls between specific numbers or agents, supporting various business workflows.
  • Manage phone numbers and assignments: Buy, update, or delete phone numbers, and bind them to agents for streamlined inbound and outbound call handling.
  • Access and review call transcripts and details: Let your agent drill down into specific calls, pulling transcripts and metadata for compliance, training, or analytics.
  • Explore and configure voice settings: Fetch detailed information about available voice options, including provider, accent, gender, and preview audio for customization of call experiences.

Supported Tools & Triggers

Tools
Add community voiceAdd a community voice from ElevenLabs to your Retell voice library.
Add sources to knowledge baseTool to add sources (documents, URLs, text) to an existing knowledge base in Retell AI.
Buy a new phone number bind agentsThis endpoint allows purchasing a new phone number with a specified area code and binding it to designated agents for inbound and outbound calls.
Create Voice AI AgentCreate a new voice AI agent with specified configuration.
Create a new outbound phone callInitiate an outbound call by POST to '/v2/create-phone-call'.
Create a new web callThe /v2/create-web-call endpoint creates a web call with a unique agent ID, returning call details like type, token, call ID, and status in JSON format, with a 201 response.
Create Batch TestTool to create a batch test job that runs multiple test cases against an agent.
Create a new chat sessionTool to create a new chat session with a chat agent.
Create a new chat agentCreate a new chat agent with specified configuration.
Create chat completionTool to create a chat completion for an existing chat session, generating the agent's response to a user message.
Create conversation flowCreate a new Conversation Flow that can be attached to an agent for response generation.
Create conversation flow componentCreates a new shared conversation flow component at POST '/create-conversation-flow-component'.
Create a new knowledge baseTool to create a new knowledge base in Retell AI with texts, files, and URLs.
Create Retell LLM Response EngineCreate a new Retell LLM Response Engine that can be attached to an agent.
Create Test Case DefinitionTool to create a test case definition for agent QA testing in Retell AI.
Delete agentDeletes an existing agent by its unique identifier.
Delete callDelete a specific call and its associated data by call ID.
Delete chat agentDelete an existing chat agent by its unique identifier.
Delete conversation flowDelete a conversation flow and all its versions.
Delete conversation flow componentDelete a shared conversation flow component.
Delete knowledge baseDelete an existing knowledge base by its unique identifier.
Delete knowledge base sourceDelete an existing source from a knowledge base.
Delete phone numberTool to delete an existing phone number from Retell AI.
Delete Retell LLMDelete an existing Retell LLM Response Engine by its unique identifier.
Delete test case definitionDelete a test case definition by its unique identifier.
End chatTool to end an active chat session.
Retrieve details of a specific agentRetrieve details of a specific agent by its unique identifier.
Get agent versionsTool to retrieve all versions of a specific agent.
Get batch testRetrieve details and results of a specific batch test job.
Get chat detailsTool to retrieve details of a specific chat session by chat ID.
Retrieve details of a specific chat agentRetrieve details of a specific chat agent by its unique identifier.
Get all versions of a chat agentRetrieve all versions of a specific chat agent by its unique identifier.
Get concurrencyRetrieves the current concurrency and concurrency limits for the organization.
Get Conversation FlowRetrieve details of a specific Conversation Flow by its ID.
Get conversation flow componentRetrieves a shared conversation flow component by its unique identifier.
Get knowledge baseRetrieve details of a specific knowledge base by its unique identifier.
Retrieve details of a specific Retell LLMRetrieve details of a specific Retell LLM Response Engine by its unique identifier.
Import phone numberTool to import a phone number from custom telephony and bind agents to it.
List agentsRetrieves a list of all agents associated with the account.
List all chatsTool to retrieve a list of all chats associated with the account.
List all phone numbersRetrieves a list of all phone numbers associated with the account.
List batch testsTool to list batch test jobs for a response engine.
List chat agentsTool to retrieve a list of all chat agents associated with the account.
List conversation flow componentsRetrieves a list of all shared conversation flow components.
List conversation flowsTool to list all conversation flows that can be attached to an agent.
List knowledge basesTool to retrieve all knowledge bases associated with the account.
List Retell LLMsTool to list all Retell LLM Response Engines that can be attached to an agent.
List test case definitionsTool to list test case definitions for a response engine (Retell LLM or Conversation Flow).
List test runsTool to list all test case jobs (test runs) for a batch test job.
List voicesList all voices available to the user.
Publish agentPublishes the latest version of the agent and creates a new draft agent with a newer version.
Publish chat agentPublishes the latest version of the chat agent and creates a new draft chat agent with a newer version.
Register phone callRegister a phone call for custom telephony integration with Retell AI.
Retrieve call detailsTool to retrieve call details with filtering options.
Retrieve call details by idRetrieve call details by ID for web/phone calls, including type, agent ID, status, timestamps, and web access token; covering responses from success to server errors.
Retrieve phone number detailsTool to retrieve details of a specific phone number from Retell AI.
Retrieve details of a specific voiceTool to retrieve details of a specific voice by its voice_id.
Search community voiceSearch for community voices from voice providers.
Update agentUpdate an existing agent's latest draft version.
Update callUpdate an active call's parameters such as metadata, dynamic variables, or data storage settings.
Update chat agentUpdate an existing chat agent configuration.
Update chat metadataTool to update metadata and sensitive data storage settings for an existing chat.
Update conversation flowUpdate an existing conversation flow configuration.
Update conversation flow componentUpdate an existing shared conversation flow component by its ID.
Update phone number configurationUpdate agent bound to a purchased phone number.
Update Retell LLM Response EngineUpdate an existing Retell LLM Response Engine by its unique identifier.
Update test case definitionUpdate a test case definition for agent testing.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting, make sure you have:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key.
  • You need credits or a connected billing setup to use the models.
  • Store the key somewhere safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Go to Settings and copy your API key.
  • This key lets your Mastra agent talk to Composio and reach Retellai through MCP.

Install dependencies

bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

  • @composio/core is the Composio SDK for creating MCP sessions
  • @mastra/core provides the Agent class
  • @mastra/mcp is Mastra's MCP client
  • @ai-sdk/openai is the model wrapper for OpenAI
  • dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models

Import libraries and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) 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 as string,
});
What's happening:
  • dotenv/config auto loads your .env so process.env.* is available
  • openai gives you a Mastra compatible model wrapper
  • Agent is the Mastra agent that will call tools and produce answers
  • MCPClient connects Mastra to your Composio MCP server
  • Composio is used to create a Tool Router session

Create a Tool Router session for Retellai

typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["retellai"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Retellai MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "retellai" for Retellai access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to

Configure Mastra MCP client and fetch tools

typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
What's happening:
  • MCPClient takes an id for this client and a list of MCP servers
  • The headers property includes the x-api-key for authentication
  • getTools fetches the tool definitions exposed by the Retellai toolkit

Create the Mastra agent

typescript
const agent = new Agent({
    name: "retellai-mastra-agent",
    instructions: "You are an AI agent with Retellai tools via Composio.",
    model: "openai/gpt-5",
  });
What's happening:
  • Agent is the core Mastra agent
  • name is just an identifier for logging and debugging
  • instructions guide the agent to use tools instead of only answering in natural language
  • model uses openai("gpt-5") to configure the underlying LLM

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\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({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        retellai: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • messages keeps the full conversation history in Mastra's expected format
  • agent.generate runs the agent with conversation history and Retellai toolsets
  • maxSteps limits how many tool calls the agent can take in a single run
  • onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

Here's the complete code to get you started with Retellai and Mastra AI:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) 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 as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["retellai"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      retellai: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "retellai-mastra-agent",
    instructions: "You are an AI agent with Retellai tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { retellai: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Retellai through Composio's Tool Router. You can extend this further by:
  • Adding other toolkits like Gmail, Slack, or GitHub
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows

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FAQ

What are the differences in Tool Router MCP and Retellai MCP?

With a standalone Retellai MCP server, the agents and LLMs can only access a fixed set of Retellai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Retellai and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with Mastra AI?

Yes, you can. Mastra AI 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 Retellai tools.

Can I manage the permissions and scopes for Retellai while using Tool Router?

Yes, absolutely. You can configure which Retellai 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 Retellai data and credentials are handled as safely as possible.

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