How to integrate Swaggerhub MCP with CrewAI

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Introduction

This guide walks you through connecting Swaggerhub to CrewAI using the Composio tool router. By the end, you'll have a working Swaggerhub agent that can list all apis i have access to, create a new api named petstore, update the description for my orders api through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Swaggerhub account through Composio's Swaggerhub 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 Swaggerhub connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Swaggerhub
  • Build a conversational loop where your agent can execute Swaggerhub 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 Swaggerhub MCP server, and what's possible with it?

The Swaggerhub MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Swaggerhub account. It provides structured and secure access so your agent can perform Swaggerhub operations on your behalf.

Supported Tools & Triggers

Tools
Add Access Control for TeamsTool to assign access control roles to teams on a SwaggerHub resource.
Add Access Control for UsersTool to assign access control roles to users on a SwaggerHub resource.
Delete Table of Contents EntryTool to delete a table of contents entry from SwaggerHub portal.
Get Access Control UsersTool to retrieve the list of users assigned access control on a SwaggerHub resource.
Get API Default VersionTool to get the default version identifier of a SwaggerHub API.
Get API VersionsTool to retrieve a list of API versions for a specific API in SwaggerHub.
Get Consumer ProductsTool to get a list of products that are visible to the consumer in a SwaggerHub portal.
Get API DefinitionTool to get the OpenAPI definition of a specified API version from SwaggerHub.
Get Domain Default VersionTool to retrieve the default version identifier of a SwaggerHub domain.
Get domain definitionTool to retrieve the OpenAPI definition of a specified domain version from SwaggerHub.
Get Domain JSON DefinitionTool to retrieve the OpenAPI definition for a specified domain version in JSON format.
Get Domain Lifecycle SettingsTool to get the published status for a specific domain and version in SwaggerHub.
Get Domain Private SettingsTool to retrieve the visibility (public or private) of a domain version in SwaggerHub.
Get Domain VersionsTool to get a list of domain versions from SwaggerHub.
Get Domain YAML DefinitionTool to retrieve the OpenAPI definition for a specified domain version in YAML format from SwaggerHub.
Get JSON API DefinitionTool to download OpenAPI definition as a JSON file from SwaggerHub Portal API.
Get JSON DefinitionTool to get the OpenAPI definition for a specified API version in JSON format.
Get lifecycle settingsTool to get the published status for the specified API and version.
Get Organization MembersTool to retrieve a list of organization members and their roles from SwaggerHub.
Get User OrganizationsTool to get organizations for a user.
Get Organization Projects V2Tool to get all projects of an organization in SwaggerHub.
Get Owner APIsTool to get a list of APIs for a specified owner in SwaggerHub.
Get owner domainsTool to retrieve domains owned by a specific SwaggerHub user or organization.
Get PortalTool to retrieve information about a portal.
Get Portal Access RequestsTool to retrieve access requests for a portal in SwaggerHub.
Get Portal AttachmentTool to get informational attachment metadata from SwaggerHub Portal.
Get Portal ProductTool to retrieve detailed information about a specific product resource.
Get Portal ProductsTool to get products for a specific portal that match your criteria.
Get PortalsTool to search for available portals.
Get Portal TemplatesTool to get templates for a specific portal that match your criteria.
Get API Version Private SettingsTool to get the visibility (public or private) of an API version.
List Resource Types and RolesTool to list available resource types and assignable roles for each in a SwaggerHub organization.
Get TemplatesTool to retrieve a list of templates for an owner in SwaggerHub.
Get User RolesTool to retrieve all roles assigned to a user across organization resources in SwaggerHub.
Get YAML API DefinitionTool to download OpenAPI definition as a YAML file from SwaggerHub Portal API.
Get YAML DefinitionTool to get the OpenAPI definition in YAML format for the specified API version from SwaggerHub.
List AttachmentsTool to retrieve all attachments for a portal or product.
Remove Access Control for TeamsTool to remove access control for teams from a SwaggerHub resource.
Remove Access Control For UsersTool to remove access control for users from a SwaggerHub organizational resource.
Remove Organization MembersTool to remove members from a SwaggerHub organization.
Search APIsTool to search SwaggerHub APIs.
Search APIs and DomainsTool to search SwaggerHub APIs, domains, and templates.
Search DomainsTool to search SwaggerHub domains.
Search Published PortalTool to search published portal content.
Update Access Control for TeamsTool to update access control roles for teams on a SwaggerHub resource.
Update Access Control for UsersTool to update access control roles for users on a SwaggerHub resource.
Update Access Control for TeamsTool to update access control for teams on a SwaggerHub resource.
Update Access Control UsersTool to update access control roles for users on a SwaggerHub resource.
Update PortalTool to update specific portal information in SwaggerHub.

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 Swaggerhub 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[mcp] python-dotenv
What's happening:
  • composio connects your agent to Swaggerhub via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] 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
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
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 Swaggerhub MCP URL

Create a Composio Tool Router session for Swaggerhub

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["swaggerhub"])

url = session.mcp.url
What's happening:
  • You create a Swaggerhub only session through Composio
  • Composio returns an MCP HTTP URL that exposes Swaggerhub tools

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\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"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[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:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

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

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["swaggerhub"],
)
url = session.mcp.url

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

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\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"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

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

Conclusion

You now have a CrewAI agent connected to Swaggerhub through Composio's Tool Router. The agent can perform Swaggerhub 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 Swaggerhub MCP Agent with another framework

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LangChain

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Vercel AI SDK

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Mastra AI

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LlamaIndex

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Claude Code

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Codex

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FAQ

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

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

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

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

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