How to integrate Svix MCP with CrewAI

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Introduction

This guide walks you through connecting Svix to CrewAI using the Composio tool router. By the end, you'll have a working Svix agent that can list all webhook endpoints for app x, create a new webhook endpoint for payments, update application rate limit to 1000/min, get delivery attempts for message id 12345 through natural language commands.

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

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

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Svix connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Svix
  • Build a conversational loop where your agent can execute Svix operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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

The Svix MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Svix account. It provides structured and secure access to your webhooks infrastructure, so your agent can perform actions like managing applications, configuring endpoints, sending webhooks, and monitoring delivery attempts on your behalf.

  • Application management and automation: Ask your agent to create, update, list, or delete Svix applications, making it easy to manage webhook-enabled projects programmatically.
  • Endpoint configuration: Have your agent register, retrieve, or remove webhook endpoints for your applications, ensuring your events get delivered to the right places.
  • Webhook delivery tracking: Let your agent fetch detailed information about message delivery attempts, helping you monitor reliability and debug failed webhooks with ease.
  • Comprehensive application insights: Retrieve metadata and details for any Svix application, so your agent can surface key info or audit your webhook ecosystem.
  • Automated cleanup and maintenance: Direct your agent to delete outdated applications or endpoints, streamlining your webhook management and reducing clutter.

Supported Tools & Triggers

Tools
Create ApplicationTool to create a new svix application.
Delete Svix ApplicationTool to delete an application by its id.
Get ApplicationTool to retrieve details of a specific svix application by its id.
List ApplicationsTool to list all applications.
Update Svix ApplicationTool to update an existing svix application by id.
Get Attempt DetailsTool to retrieve details of a specific message attempt.
List Message AttemptsTool to list all delivery attempts for a specific message.
Create EndpointTool to create a new svix webhook endpoint.
Delete EndpointTool to delete an endpoint.
Get EndpointTool to retrieve details of a specific endpoint.
List EndpointsTool to list all endpoints for a specific application.
Patch EndpointTool to partially update an endpoint’s configuration.
Patch Endpoint HeadersTool to partially update headers for a specific endpoint.
Recover Failed WebhooksTool to recover messages that failed to send to an endpoint.
Replay Missing WebhooksTool to replay missing webhooks for a specific endpoint.
Get Endpoint SecretTool to retrieve the secret for a specific endpoint.
Rotate Endpoint SecretTool to rotate the signing secret key for an endpoint.
Send Example MessageTool to send a test message for a specific event type to an endpoint.
Get Endpoint StatsTool to retrieve basic statistics for a specific endpoint.
Get Endpoint TransformationTool to retrieve transformation settings for a specific endpoint.
Set Endpoint TransformationTool to set or update transformation settings for an endpoint.
Update EndpointTool to update an existing endpoint.
Update Endpoint HeadersTool to completely replace headers for a specific endpoint.
Create Event TypeTool to create a new event type or unarchive an existing one.
Delete Event TypeTool to delete an event type.
Get Event TypeTool to retrieve details of a specific event type by its id.
List Event TypesTool to retrieve a list of all event types.
Update Event TypeTool to update an existing event type by id.
Create IntegrationTool to create a new integration for a specific application.
Delete IntegrationTool to delete an integration.
Get IntegrationTool to retrieve details of a specific integration.
List IntegrationsTool to list all integrations for a specific application.
Update IntegrationTool to update an existing integration by id.
Create MessageTool to create a new message for a specific application in svix.
Get MessageTool to retrieve details of a specific message by its id.
List MessagesTool to list all messages for a specific application.
Create SourceTool to create a source for message ingestion.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Svix connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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.

Install dependencies

bash
pip install composio crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Svix via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

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_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Svix MCP URL

Create a Composio Tool Router session for Svix

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["svix"],
)
url = session.mcp.url
What's happening:
  • You create a Svix only session through Composio
  • Composio returns an MCP HTTP URL that exposes Svix tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Svix Assistant",
    goal="Help users interact with Svix through natural language commands",
    backstory=(
        "You are an expert assistant with access to Svix tools. "
        "You can perform various Svix operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Svix MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Svix operations.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Svix related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_agent],
        tasks=[task],
        verbose=False,
    )

    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's happening:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_svix_agent.py

Complete Code

Here's the complete code to get you started with Svix and CrewAI:

python
# file: crewai_svix_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

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

    # Configure LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create Svix assistant agent
    toolkit_agent = Agent(
        role="Svix Assistant",
        goal="Help users interact with Svix through natural language commands",
        backstory=(
            "You are an expert assistant with access to Svix tools. "
            "You can perform various Svix operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Svix operations.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Svix related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Svix through Composio's Tool Router. The agent can perform Svix operations through natural language commands. Next steps:
  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

How to build Svix MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Svix MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Svix MCP with Claude Agent SDK

Google ADK

Google ADK

Use Svix MCP with Google ADK

LangChain

LangChain

Use Svix MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Svix MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Svix MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Svix MCP with LlamaIndex

FAQ

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

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

Can I use Tool Router MCP with CrewAI?

Yes, you can. CrewAI 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 Svix tools.

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

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

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