# How to integrate Remote retrieval MCP with CrewAI

```json
{
  "title": "How to integrate Remote retrieval MCP with CrewAI",
  "toolkit": "Remote retrieval",
  "toolkit_slug": "remote_retrieval",
  "framework": "CrewAI",
  "framework_slug": "crew-ai",
  "url": "https://composio.dev/toolkits/remote_retrieval/framework/crew-ai",
  "markdown_url": "https://composio.dev/toolkits/remote_retrieval/framework/crew-ai.md",
  "updated_at": "2026-05-12T10:23:41.560Z"
}
```

## Introduction

This guide walks you through connecting Remote retrieval to CrewAI using the Composio tool router. By the end, you'll have a working Remote retrieval agent that can show all pending laptop return orders, get company info for acme corp, validate your remote retrieval api key through natural language commands.
This guide will help you understand how to give your CrewAI agent real control over a Remote retrieval account through Composio's Remote retrieval MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Remote retrieval with

- [OpenAI Agents SDK](https://composio.dev/toolkits/remote_retrieval/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/remote_retrieval/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/remote_retrieval/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/remote_retrieval/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/remote_retrieval/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/remote_retrieval/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/remote_retrieval/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/remote_retrieval/framework/cli)
- [Google ADK](https://composio.dev/toolkits/remote_retrieval/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/remote_retrieval/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/remote_retrieval/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/remote_retrieval/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/remote_retrieval/framework/llama-index)

## TL;DR

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

The Remote retrieval MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Remote retrieval account. It provides structured and secure access to your company’s equipment logistics, so your agent can perform actions like listing equipment return orders, validating API access, and fetching company details on your behalf.
- Comprehensive order tracking and listing: Ask your agent to retrieve a paginated list of all equipment return orders, with optional filters for status or date to keep tabs on ongoing returns.
- Company information retrieval: Let your agent pull detailed company information instantly, helping with audits, reporting, or support workflows.
- API key validation automation: Have your agent confirm that your API key is valid before performing sensitive actions, ensuring secure and authorized access every time.
- Status-based filtering of return orders: Easily filter and fetch orders by their current status (e.g., pending, completed) to streamline logistics reviews or escalation processes.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `REMOTE_RETRIEVAL_CREATE_ORDER` | Create Order | Tool to create IT asset retrieval orders for remote employees. Use when you need to schedule pickup of laptops, monitors, cell phones, or tablets from employees. Supports both return-to-company and recycle-with-data-destruction workflows. |
| `REMOTE_RETRIEVAL_GET_ALL_ORDERS` | Get All Orders | Tool to retrieve a paginated list of all orders. Use when you need to list orders with optional filters like status or dates. Call after authentication. |
| `REMOTE_RETRIEVAL_GET_COMPANY_DETAILS` | Get Company Details | Tool to retrieve detailed information for a company. Use after confirming a valid company_id. |
| `REMOTE_RETRIEVAL_GET_DEVICE_PRICES` | Get Device Prices | Tool to retrieve real-time pricing data for all supported devices. Use when you need current prices for Laptop, Monitor (17-23 inch), Monitor_27 (24-27 inch), Tablet, or Cell Phone. |
| `REMOTE_RETRIEVAL_GET_ORDER_DETAILS` | Get Order Details | Tool to retrieve specific order details by order ID. Use when you need detailed information about an order including employee info, company info, and shipment status (device_type, send_status, return_status). |
| `REMOTE_RETRIEVAL_LIST_PENDING_ORDERS` | List Pending Orders | Tool to retrieve list of all pending orders. Use when you need to check orders awaiting payment completion. Results are paginated up to 25 per page. |
| `REMOTE_RETRIEVAL_VALIDATE_USER` | RemoteRetrieval: Validate User | Tool to validate the provided API key. Use when you need to confirm the API key's validity. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Remote retrieval MCP server is an implementation of the Model Context Protocol that connects your AI agent to Remote retrieval. It provides structured and secure access so your agent can perform Remote retrieval operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

Before starting, make sure you have:
- Python 3.9 or higher
- A Composio account and API key
- A Remote retrieval connection authorized in Composio
- An OpenAI API key for the CrewAI LLM
- Basic familiarity with Python

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

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

### 3. Set up environment variables

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
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

**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 Remote retrieval MCP URL
```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")
```

### 5. Create a Composio Tool Router session for Remote retrieval

**What's happening:**
- You create a Remote retrieval only session through Composio
- Composio returns an MCP HTTP URL that exposes Remote retrieval tools
```python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["remote_retrieval"])

url = session.mcp.url
```

### 6. Initialize the MCP Server

**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.
```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,
    )
```

### 7. Create a CLI Chatloop and define the Crew

**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.
```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")
```

## Complete Code

```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=["remote_retrieval"],
)
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 Remote retrieval through Composio's Tool Router. The agent can perform Remote retrieval 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 Remote retrieval MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/remote_retrieval/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/remote_retrieval/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/remote_retrieval/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/remote_retrieval/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/remote_retrieval/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/remote_retrieval/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/remote_retrieval/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/remote_retrieval/framework/cli)
- [Google ADK](https://composio.dev/toolkits/remote_retrieval/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/remote_retrieval/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/remote_retrieval/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/remote_retrieval/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/remote_retrieval/framework/llama-index)

## Related Toolkits

- [Ashby](https://composio.dev/toolkits/ashby) - Ashby is an applicant tracking system that handles job postings, candidate management, and hiring analytics.
- [Async interview](https://composio.dev/toolkits/async_interview) - Async interview is an on-demand video interview platform for streamlined hiring. Candidates record responses on their schedule, so employers can review anytime.
- [Bamboohr](https://composio.dev/toolkits/bamboohr) - BambooHR is a cloud-based HR management platform for small and mid-sized businesses. It streamlines employee data, HR workflows, and reporting in one easy interface.
- [Breathe HR](https://composio.dev/toolkits/breathehr) - Breathe HR is cloud-based HR software for SMEs to manage employee data, absences, and performance. It simplifies HR admin, making it easy to keep employee records accurate and up to date.
- [Connecteam](https://composio.dev/toolkits/connecteam) - Connecteam is a workforce management platform for deskless teams, streamlining operations, HR, and team communication. It helps businesses save time by automating scheduling, time tracking, and staff engagement tasks.
- [Lever](https://composio.dev/toolkits/lever) - Lever is an applicant tracking system that blends sourcing, CRM, and analytics for recruiting. It helps companies scale hiring with collaborative workflows and actionable insights.
- [Recruitee](https://composio.dev/toolkits/recruitee) - Recruitee is collaborative hiring software that centralizes recruitment tasks for teams. It streamlines sourcing, interviewing, and hiring so you can fill roles faster.
- [Sap successfactors](https://composio.dev/toolkits/sap_successfactors) - Sap successfactors is a cloud-based human capital management suite for HR, payroll, recruiting, and talent management. It helps organizations centralize employee data and streamline the entire employee lifecycle.
- [Talenthr](https://composio.dev/toolkits/talenthr) - TalentHR is an intuitive, all-in-one HR tool for managing employee records, leave, and HR workflows. It streamlines HR operations so businesses can focus on people, not paperwork.
- [Workable](https://composio.dev/toolkits/workable) - Workable is an all-in-one HR software platform that streamlines hiring, employee management, and payroll. It helps teams simplify recruiting, onboarding, and staff operations in one place.
- [Workday](https://composio.dev/toolkits/workday) - Workday is a cloud-based ERP platform for HR, finance, and workforce analytics. It streamlines employee management, payroll, and business operations in a single system.
- [Gmail](https://composio.dev/toolkits/gmail) - Gmail is Google's email service with powerful spam protection, search, and G Suite integration. It keeps your inbox organized and makes communication fast and reliable.
- [Google Calendar](https://composio.dev/toolkits/googlecalendar) - Google Calendar is a time management service for scheduling meetings, events, and reminders. It streamlines personal and team organization with integrated notifications and sharing options.
- [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices.
- [Outlook](https://composio.dev/toolkits/outlook) - Outlook is Microsoft's email and calendaring platform for unified communications and scheduling. It helps users stay organized with powerful email, contacts, and calendar management.
- [Twitter](https://composio.dev/toolkits/twitter) - Twitter is a social media platform for sharing real-time updates, conversations, and news. Stay connected, informed, and engaged with communities worldwide.
- [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly.
- [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure.
- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Remote retrieval MCP?

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

### Can I manage the permissions and scopes for Remote retrieval while using Tool Router?

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

---
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