How to integrate Composio search MCP with CrewAI

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Introduction

This guide walks you through connecting Composio search to CrewAI using the Composio tool router. By the end, you'll have a working Composio search agent that can find recent news about electric vehicles, search for top-rated hotels in paris, get latest stock info for apple through natural language commands.

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

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

Also integrate Composio search with

TL;DR

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

The Composio search MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to the entire Composio Search suite. It provides structured and secure access to powerful web, travel, shopping, news, academic, and financial search tools, so your agent can perform actions like searching the web, finding events, locating places, pulling news, and fetching academic research on your behalf.

  • Comprehensive web and news search: Instantly ask your agent to fetch up-to-date web pages, breaking news, or current events using Google, DuckDuckGo, or news-specific search APIs.
  • Travel and local discovery: Let your agent find nearby hotels, flights, events, or map locations using Google Maps and events search for seamless travel planning and local exploration.
  • E-commerce and product lookup: Have your agent search for products, deals, and reviews across major retailers like Amazon and Walmart to help you shop smarter and faster.
  • Financial and market data retrieval: Direct your agent to pull real-time stock information, financial news, and market trends with just a query—no manual research needed.
  • Academic and scholarly research: Empower your agent to find relevant academic papers, citations, and scholarly articles using Google Scholar and Exa Answer for research-heavy tasks.

Supported Tools & Triggers

Tools
Amazon Product SearchSearch Amazon for products across different country marketplaces.
Composio DuckDuckGo SearchThe DuckDuckGoSearch class utilizes the Composio DuckDuckGo Search API to perform searches, focusing on web information and details.
Composio Google Events SearchSearch for upcoming events, concerts, festivals, conferences, and other activities.
Composio SimilarlinksPerform a search to find similar links and retrieve a list of relevant results.
Fetch URL ContentFetch and extract clean, readable page text (markdown) from public web pages (HTML content) using the Exa API.
Composio Finance SearchGet real-time stock prices, market data, financial news, and company information with historical analysis.
Flight SearchSearch for flights with comprehensive pricing, schedule, and airline information.
Composio Google Maps SearchPerforms a location-specific search via the Composio Google Maps Search API, returning results under `results.
Groq Chat CompletionExecute fast LLM inference using Groq's optimized hardware and API.
Hotel SearchSearch for hotels and vacation rentals with comprehensive filtering and pricing.
Composio Image SearchThe ImageSearch class performs an image search using the Composio Image Search API, targeting image metadata and URLs (not binary data) via Google Images.
Composio News SearchSearch for the latest news articles and current events with smart filtering.
NPPES NPI Registry LookupLookup US healthcare provider details from the CMS NPI Registry (NPPES) using an NPI number or search filters.
Composio Scholar SearchScholar API scrapes Google Scholar search results via SERP API, returning academic papers and scholarly articles.
Composio SEC EDGAR Filings SearchRetrieve authoritative SEC EDGAR filing metadata (10-K/10-Q/8-K etc.
Composio Shopping SearchSearch for products with advanced price filtering, location targeting, and deal discovery.
Composio LLM SearchThe Composio LLM Search class serves as a gateway to the Composio LLM Search API, allowing users to perform searches across a broad range of content with multiple filtering options.
Composio Trends SearchDiscover trending topics, search patterns, and popularity data.
TripAdvisor Travel SearchSearch TripAdvisor for travel recommendations and itinerary planning without authentication (unlike TRIPADVISOR_CONTENT_API_SEARCH_LOCATIONS and other TripAdvisor tools requiring an active connection).
Vercel AI Gateway ChatExecute LLM inference through Vercel AI Gateway's unified API.
Walmart Product SearchSearch Walmart for products with price filtering.
Composio Web SearchPerform a web search using the Exa API.

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

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Composio search 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 Composio search 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 Composio search MCP URL

Create a Composio Tool Router session for Composio search

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

url = session.mcp.url
What's happening:
  • You create a Composio search only session through Composio
  • Composio returns an MCP HTTP URL that exposes Composio search 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 Composio search 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=["composio_search"],
)
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 Composio search through Composio's Tool Router. The agent can perform Composio search 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

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FAQ

What are the differences in Tool Router MCP and Composio search MCP?

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

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

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

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