How to integrate Webex MCP with Pydantic AI

This guide walks you through connecting Webex to Pydantic AI using the Composio tool router. By the end, you'll have a working Webex agent that can create a new project discussion room, list all teams i am part of, send a meeting summary to your team through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Webex account through Composio's Webex MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Webex is a Cisco-powered video conferencing and team collaboration platform. It streamlines online meetings, webinars, and secure team messaging for organizations.

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Introduction

This guide walks you through connecting Webex to Pydantic AI using the Composio tool router. By the end, you'll have a working Webex agent that can create a new project discussion room, list all teams i am part of, send a meeting summary to your team through natural language commands.

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

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

Also integrate Webex with

ChatGPTOpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenCodeCursorVS CodeOpenClawHermesCLIGoogle 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 Webex
  • 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 Webex 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 Webex MCP server, and what's possible with it?

The Webex MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Webex account. It provides structured and secure access to your meetings, teams, rooms, and messaging, so your agent can perform actions like managing teams, creating rooms, posting messages, and handling memberships on your behalf.

  • Automated team and room management: Quickly create new teams or rooms, group conversations by topic, and keep collaboration spaces organized through your agent.
  • Seamless messaging and announcements: Direct your agent to post messages, send important files, or share updates with individuals or entire rooms instantly.
  • Membership and access control: Effortlessly add or remove members from teams and spaces, ensuring the right people have access at the right time.
  • Webhook and event integrations: List and manage Webex webhooks so your agent can react to events or changes as they happen in your workspace.
  • Content and message cleanup: Ask your agent to delete outdated or mistaken messages and memberships, keeping your workspace tidy and relevant.

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 Webex
  • 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 Webex
  • MCPServerStreamableHTTP connects to the Webex 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 Webex
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["webex"],
    )
    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 Webex 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
webex_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[webex_mcp],
    instructions=(
        "You are a Webex assistant. Use Webex tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Webex endpoint
  • The agent uses GPT-5 to interpret user commands and perform Webex 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 Webex.\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
  • Webex 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 Webex 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 Webex
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["webex"],
    )
    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
    webex_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[webex_mcp],
        instructions=(
            "You are a Webex assistant. Use Webex 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 Webex.\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 Webex through Composio's Tool Router. With this setup, your agent can perform real Webex 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 + Webex 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 Webex action and event your agent gets out of the box.

Create Team

Creates a new Webex team and automatically adds the authenticated user as a member.

Get Team Details

Tool to retrieve details for a specific team by teamId.

List Teams

Lists all teams the authenticated user belongs to.

List Webhooks

Lists all webhooks registered for the authenticated user or organization.

Create Message

Tool to post a message to a Webex room or person.

Create Room

Creates a new Webex room for team collaboration.

Create Team Membership

Tool to add a person to a Webex team by personId or personEmail.

Delete Membership

Tool to delete a Webex membership by its unique identifier.

Delete Message

Tool to delete a Webex message by its unique identifier.

Delete Room

Deletes a Webex room by its ID.

Get Membership Details

Tool to retrieve details for a specific membership.

Get Message Details

Tool to retrieve details for a specific message.

Get Team Membership Details

Tool to retrieve details for a specific team membership.

List Memberships

List memberships in Webex rooms.

List Messages

Tool to list messages in a room.

List Rooms

Tool to list rooms the authenticated user belongs to.

List Team Memberships

Tool to list all memberships for a specific team.

Get Person Details

Retrieves detailed profile information for a specific person by their ID.

List People

Tool to list people in your organization.

Get Room Details

Tool to retrieve details for a specific room.

Update Membership

Updates a Webex room membership by ID to change moderator or monitor status.

Update Room

Update a room's title, lock status, or team association.

Update Team

Tool to update a team's name by teamId.

Create Webhook

Creates a Webex webhook to receive real-time event notifications via HTTP POST requests to your specified URL.

Delete Webhook

Tool to delete a specific webhook.

Get Webhook Details

Retrieves detailed information about a specific Webex webhook by its ID.

FRAMEWORKS

How to build Webex MCP Agent with another framework

ChatGPT logo

ChatGPT

Use Webex MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Webex MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Webex MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Webex MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Webex MCP with Claude Cowork

Codex logo

Codex

Use Webex MCP with Codex

Cursor logo

Cursor

Use Webex MCP with Cursor

OpenClaw logo

OpenClaw

Use Webex MCP with OpenClaw

Hermes logo

Hermes

Use Webex MCP with Hermes

CLI logo

CLI

Use Webex MCP with CLI

Google ADK logo

Google ADK

Use Webex MCP with Google ADK

LangChain logo

LangChain

Use Webex MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Webex MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Webex MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Webex MCP with LlamaIndex

CrewAI logo

CrewAI

Use Webex MCP with CrewAI

OpenCode logo

OpenCode

Use Webex MCP with OpenCode

VS Code logo

VS Code

Use Webex MCP with VS Code

FAQ

Frequently asked questions

With a standalone Webex MCP server, the agents and LLMs can only access a fixed set of Webex tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Webex 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 Webex tools.

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

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