How to integrate Endorsal MCP with Pydantic AI

This guide walks you through connecting Endorsal to Pydantic AI 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 Pydantic 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.

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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 Pydantic AI 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 Pydantic 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.

Also integrate Endorsal with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Endorsal
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Endorsal workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
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 pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Endorsal
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Endorsal
  • MCPServerStreamableHTTP connects to the Endorsal MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Endorsal
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["endorsal"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Endorsal tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
endorsal_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[endorsal_mcp],
    instructions=(
        "You are a Endorsal assistant. Use Endorsal tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Endorsal endpoint
  • The agent uses GPT-5 to interpret user commands and perform Endorsal operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Endorsal.\n")

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", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Endorsal API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

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

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Endorsal
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["endorsal"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    endorsal_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[endorsal_mcp],
        instructions=(
            "You are a Endorsal assistant. Use Endorsal tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Endorsal.\n")

    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", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

Conclusion

You've built a Pydantic AI agent that can interact with Endorsal through Composio's Tool Router. With this setup, your agent can perform real Endorsal actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Endorsal for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
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. Pydantic 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 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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