How to integrate Endorsal MCP with Autogen

This guide walks you through connecting Endorsal to AutoGen using the Composio tool router. By the end, you'll have a working Endorsal agent that can add new customer testimonial from recent feedback, list all active autorequest campaigns, show all testimonials submitted by this contact through natural language commands. This guide will help you understand how to give your AutoGen 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.

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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.

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Introduction

This guide walks you through connecting Endorsal to AutoGen using the Composio tool router. By the end, you'll have a working Endorsal agent that can add new customer testimonial from recent feedback, list all active autorequest campaigns, show all testimonials submitted by this contact through natural language commands.

This guide will help you understand how to give your AutoGen 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.

Also integrate Endorsal with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Endorsal
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Endorsal tools
  • Run a live chat loop where you ask the agent to perform Endorsal operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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 step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Endorsal account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Endorsal via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Endorsal connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Endorsal session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["endorsal"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Endorsal tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Endorsal assistant agent with MCP tools
    agent = AssistantAgent(
        name="endorsal_assistant",
        description="An AI assistant that helps with Endorsal operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Endorsal tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Endorsal related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Endorsal tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Endorsal and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Endorsal session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["endorsal"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Endorsal assistant agent with MCP tools
        agent = AssistantAgent(
            name="endorsal_assistant",
            description="An AI assistant that helps with Endorsal operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Endorsal related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Endorsal through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Endorsal, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
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

Retrieves a paginated list of all contacts for a specific property in your Endorsal account.

List Contact Testimonials

Retrieves all testimonials associated with a specific contact in Endorsal.

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

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Endorsal MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Endorsal MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Endorsal MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Endorsal MCP with Claude Cowork

Codex logo

Codex

Use Endorsal MCP with Codex

OpenClaw logo

OpenClaw

Use Endorsal MCP with OpenClaw

Hermes logo

Hermes

Use Endorsal MCP with Hermes

CLI logo

CLI

Use Endorsal MCP with CLI

Google ADK logo

Google ADK

Use Endorsal MCP with Google ADK

LangChain logo

LangChain

Use Endorsal MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Endorsal MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Endorsal MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Endorsal MCP with LlamaIndex

CrewAI logo

CrewAI

Use Endorsal MCP with CrewAI

FAQ

Frequently asked questions

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

Yes, you can. Autogen 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 Endorsal tools.

Yes, absolutely. You can configure which Endorsal 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.

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 Endorsal data and credentials are handled as safely as possible.

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