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How to integrate Endorsal MCP with Mastra AI

Connect Mastra AI to Endorsal MCP. Add new customer testimonial from recent feedback, list all active autorequest campaigns, and more using natural language, with authentication handled for you.

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Endorsal automates the collection and display of customer testimonials and reviews. Boost your business's credibility and social proof with effortless feedback management.

26 Tools

Introduction

This guide walks you through connecting Endorsal to Mastra AI using the Composio tool router. By the end, you'll have a working Endorsal agent that can create and update testimonials, search and tag testimonials, manage contacts, list AutoRequest campaigns, and fetch display widgets through natural language commands.

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

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

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Endorsal with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKGrok BotCodexOpenClawHermesAtomic AgentGoogle ADKLangChainVercel AI SDKLlamaIndexCrewAIPydantic AIAutoGen

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 Endorsal tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Endorsal 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 Endorsal 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 Endorsal MCP server, and what's possible with it?

The Endorsal MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Endorsal account. It provides structured and secure access to your testimonials, contacts, and campaign data, so your agent can perform actions like collecting new testimonials, managing contacts, organizing campaigns, and displaying review widgets on your behalf.

  • Automated testimonial collection and submission: Enable your agent to create and submit new customer testimonials directly into your Endorsal account, streamlining the feedback process.
  • Campaign management and insights: Let your agent retrieve and list all AutoRequest campaigns, check campaign details, and monitor their status to keep your outreach efforts on track.
  • Contact and testimonial organization: Easily fetch, list, and manage all contacts, as well as pull up all testimonials associated with a specific contact for better relationship tracking.
  • Widget and property access: Allow your agent to fetch details of specific display widgets and properties, helping you control how testimonials are showcased on your website.
  • Tag and metadata retrieval: Retrieve tag details and full testimonial metadata, so your agent can help organize, group, or analyze your customer feedback efficiently.

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

Step by step09 STEPS
1

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
2

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 Endorsal through MCP.
3

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
4

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
5

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
6

Create a Tool Router session for Endorsal

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

  const composioMCPUrl = session.mcp.url;
  console.log("Endorsal MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "endorsal" for Endorsal access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to
7

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 Endorsal toolkit
8

Create the Mastra agent

typescript
const agent = new Agent({
    name: "endorsal-mastra-agent",
    instructions: "You are an AI agent with Endorsal 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
9

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: {
        endorsal: 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 Endorsal 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 Endorsal 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: ["endorsal"],
  });

  const composioMCPUrl = session.mcp.url;

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

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "endorsal-mastra-agent",
    instructions: "You are an AI agent with Endorsal 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: { endorsal: 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 Endorsal 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
TOOLS

Supported Tools

Every Endorsal action and event your agent gets out of the box.

Archive Contact

Tool to archive a contact using its unique identifier.

Create Contact

Tool to create a new contact in your Endorsal account.

Create Tag

Tool to create a new tag in Endorsal.

Create Testimonial

Tool to submit a new testimonial.

Delete Tag

Tool to delete a tag using its unique identifier.

Delete Testimonial

Tool to permanently delete a testimonial by its ID.

Get AutoRequest Campaign

Tool to retrieve a specific AutoRequest campaign by its unique identifier.

Get Contact

Tool to retrieve details of a specific contact by its unique identifier.

Get Tag

Tool to retrieve details of a specific tag by its unique identifier.

Get Testimonial

Retrieves complete details of a specific testimonial by its ID.

Get Wall of Love

Retrieves the Wall of Love for a property, returning testimonials that match its configuration options.

Get Widget

Tool to retrieve details of a specific widget by its unique identifier.

List All Tags

Tool to retrieve a list of all Tag Objects across all properties in your Endorsal account.

List AutoRequest Campaigns

Tool to retrieve a list of all AutoRequest campaigns.

List Contacts

Lists contacts for a specified Endorsal property, with optional pagination, to browse contact details.

List Contact Testimonials

Retrieves testimonials associated with a specific Endorsal contact, with optional pagination.

List Properties

Tool to retrieve all properties for the authenticated account.

List Tags

Retrieves all tags associated with a specific property in Endorsal.

List Tag Testimonials

Tool to retrieve all testimonials for a given tag.

List Testimonials

Retrieves a paginated list of all testimonials in your Endorsal account.

List Widgets

Retrieves all testimonial display widgets associated with your Endorsal account.

Search Contacts

Tool to search contacts using query Match Objects.

Search Testimonials

Tool to search testimonials using query Match Objects.

Tag Testimonial

Tool to add tag(s) to a testimonial.

Update Contact

Tool to update a contact's information.

Update Testimonial

Tool to update an existing testimonial.

FRAMEWORKS

How to build Endorsal MCP Agent with another framework

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Claude Code

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Claude Cowork

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OpenAI Agents SDK logo

OpenAI Agents SDK

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Claude Agent SDK logo

Claude Agent SDK

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Grok Bot logo

Grok Bot

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Codex logo

Codex

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OpenClaw logo

OpenClaw

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Hermes logo

Hermes

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Atomic Agent logo

Atomic Agent

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Google ADK logo

Google ADK

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LangChain logo

LangChain

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Vercel AI SDK logo

Vercel AI SDK

Use Endorsal MCP with Vercel AI SDK

LlamaIndex logo

LlamaIndex

Use Endorsal MCP with LlamaIndex

CrewAI logo

CrewAI

Use Endorsal MCP with CrewAI

Pydantic AI logo

Pydantic AI

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AutoGen logo

AutoGen

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FAQ

Frequently asked questions

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