# How to integrate Codereadr MCP with CrewAI

```json
{
  "title": "How to integrate Codereadr MCP with CrewAI",
  "toolkit": "Codereadr",
  "toolkit_slug": "codereadr",
  "framework": "CrewAI",
  "framework_slug": "crew-ai",
  "url": "https://composio.dev/toolkits/codereadr/framework/crew-ai",
  "markdown_url": "https://composio.dev/toolkits/codereadr/framework/crew-ai.md",
  "updated_at": "2026-05-12T10:07:02.660Z"
}
```

## Introduction

This guide walks you through connecting Codereadr to CrewAI using the Composio tool router. By the end, you'll have a working Codereadr agent that can create a new barcode scanning service, configure survey questions after each scan, enable kiosk mode for unattended device through natural language commands.
This guide will help you understand how to give your CrewAI agent real control over a Codereadr account through Composio's Codereadr MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Codereadr with

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

## TL;DR

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

The Codereadr MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Codereadr account. It provides structured and secure access to your data collection and barcode scanning workflows, so your agent can create services, configure scan workflows, manage databases, and automate data collection processes for you.
- Automated service and workflow setup: Let your agent create new CodeREADr services and configure custom workflows for scanning, picking, delivery, and receiving tasks.
- Custom data collection form creation: Easily set up or modify data capture forms by adding or deleting custom questions after each scan.
- Real-time scan integration: Configure Direct Scan URLs, postback endpoints, or Google Sheets connectors to forward scan results instantly to your desired platforms.
- Device and database management: Direct your agent to delete devices or entire databases when they are no longer needed, streamlining your data environment.
- Kiosk and unattended scanning configuration: Enable and fine-tune Kiosk Mode for unattended or dedicated scanning stations to support high-volume operations.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `CODEREADR_COLLECT_DATA_WITH_QUESTIONS` | Collect Data With Questions | Create and attach custom questions to a CodeREADr service for data collection after scans. Use this to configure forms that collect additional information from users after each barcode scan. Requires a valid service ID from CODEREADR_RETRIEVE_SERVICES or CODEREADR_CREATE_SERVICE. |
| `CODEREADR_CONFIGURE_CONNECTOR` | Configure CodeREADr Connector | Helper to guide configuring the CodeREADr Connector for Google Sheets. There is no public API to programmatically create connector configurations. This tool validates your API connectivity (optional) and returns clear steps to proceed via the Google Sheets Add-on UI: https://www.codereadr.com/knowledgebase/codereadr-connector-add-on/ |
| `CODEREADR_CREATE_SERVICE` | Create CodeREADr Service | Creates a new CodeREADr service (barcode scanning workflow configuration). A service defines how barcode scans are processed - whether they're simply recorded, validated against a database, forwarded to an external URL, or display web content. Each validation_method type has different required parameters: 'database'/'ondevicedatabase' require database_id, 'postback' requires postback_url, 'webview' requires description (URL/HTML). |
| `CODEREADR_DELETE_DATABASE` | Delete CodeREADr Database | Delete a CodeREADr validation database by its ID. This permanently removes the database and all its barcode values. Use with caution. Note: A database cannot be deleted if it is currently linked to one or more services. You must unlink those services from the database first. Example: "Delete database with ID 1340798" |
| `CODEREADR_DELETE_DEVICE` | Delete Device | Tool to delete a device from CodeREADr. Uses the CodeREADr legacy API with section=devices and action=delete parameters. Note: Device deletion may have limited support in the CodeREADr API - only 'retrieve' and 'update' actions are officially documented for devices. |
| `CODEREADR_DELETE_QUESTION` | Delete Custom Question | Permanently deletes one or more custom questions from your CodeREADr account. Questions are used to collect additional data after scans. Once deleted, the question and all associated answer options are removed. This action cannot be undone. |
| `CODEREADR_DELETE_SERVICE` | Delete CodeREADr Service | Delete a CodeREADr service by its numeric ID. Use this to permanently remove a service/workflow configuration from your account. Note: This is a destructive action and cannot be undone. You can delete a single service, multiple services (comma-separated IDs), or all services. Example: "Delete service with ID 12345" |
| `CODEREADR_DELETE_USER` | Delete CodeREADr User | Deletes an existing user account from CodeREADr. Uses the CodeREADr legacy API endpoint (POST /api/ with section=users, action=delete). The user_id parameter can be a single ID, comma-separated list of IDs, or 'all'. Note: You cannot delete the account owner's app-user. The API will return an error if an invalid user_id is provided. |
| `CODEREADR_GENERATE_SCAN_LINK` | Generate Scan Link | Generates a CodeREADr scan link URI that opens the CodeREADr mobile app with a pre-filled scan value. Use this tool when you need to create clickable links that launch the CodeREADr scanner with a specific barcode, QR code, or identifier already entered. |
| `CODEREADR_LIST_SUPPORTED_BARCODE_TYPES` | List Supported Barcode Types | Lists barcode symbologies supported by CodeREADr for scanning. Returns 1D barcodes (Code 39, Code 128, EAN, UPC, Codabar, etc.), 2D barcodes (QR Code, Data Matrix, PDF-417, Aztec, etc.), and specialized formats. Use this to verify if a specific barcode type is supported before scanning. |
| `CODEREADR_RETRIEVE_DATABASES` | Retrieve CodeREADr Databases | Retrieves all validation databases configured in your CodeREADr account. Use this to list databases for barcode validation, see their IDs, names, item counts, and which services they're linked to. |
| `CODEREADR_RETRIEVE_DEVICES` | Retrieve Devices | Retrieve a list of devices registered to your CodeREADr account. This tool fetches information about devices linked to your account, including device IDs, UDIDs, names, and creation timestamps. Use this to monitor which devices have access to your CodeREADr services. |
| `CODEREADR_RETRIEVE_SCANS` | Retrieve Scan Records | Retrieve scan records from your CodeREADr account. Scans are the core data collected by CodeREADr when users scan barcodes using the mobile app. Each scan record includes the barcode value, timestamp, device info, validation status, and any collected responses. Use filters to narrow down results by service, user, device, date range, or status. Returns scan records in batches. Use limit and offset parameters for pagination. |
| `CODEREADR_RETRIEVE_SERVICES` | Retrieve CodeREADr Services | Retrieve configured services from your CodeREADr account. Services are the core organizational units in CodeREADr that define how barcode scans are validated and processed. Use this action to list all services or retrieve specific services by ID. |
| `CODEREADR_UPDATE_QUESTION` | Update CodeREADr Question | Add answer options to an existing CodeREADr question. Use this to add selectable answers for checkbox, dropdown, or option-type questions. The CodeREADr API does not support updating question text - to change text, delete and recreate the question. |
| `CODEREADR_UPDATE_SERVICE` | Update CodeREADr Service | Update an existing CodeREADr service configuration. Use this action to modify settings of a service by its ID. Only specified fields will be updated - omitted fields retain their current values. Common use cases: - Renaming a service - Changing postback/webhook URL - Enabling/disabling GPS tracking - Modifying duplicate scan handling - Setting time restrictions for service availability |

## Supported Triggers

None listed.

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

The Codereadr MCP server is an implementation of the Model Context Protocol that connects your AI agent to Codereadr. It provides structured and secure access so your agent can perform Codereadr 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 Codereadr 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 Codereadr 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 Codereadr 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 Codereadr

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

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=["codereadr"],
)
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 Codereadr through Composio's Tool Router. The agent can perform Codereadr 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 Codereadr MCP Agent with another framework

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

## Related Toolkits

- [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. It's the go-to tool for calculations, reporting, and flexible data management.
- [21risk](https://composio.dev/toolkits/_21risk) - 21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters.
- [Abstract](https://composio.dev/toolkits/abstract) - Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort.
- [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time.
- [Agentql](https://composio.dev/toolkits/agentql) - Agentql is a toolkit that connects AI agents to the web using a specialized query language. It enables structured web interaction and data extraction for smarter automations.
- [Agenty](https://composio.dev/toolkits/agenty) - Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. It streamlines data collection, monitoring, and repetitive online actions.
- [Ambee](https://composio.dev/toolkits/ambee) - Ambee is an environmental data platform providing real-time, hyperlocal APIs for air quality, weather, and pollen. Get precise environmental insights to power smarter decisions in your apps and workflows.
- [Ambient weather](https://composio.dev/toolkits/ambient_weather) - Ambient Weather is a platform for personal weather stations with a robust API for accessing local, real-time, and historical weather data. Get detailed environmental insights directly from your own sensors for smarter apps and automations.
- [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements.
- [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints.
- [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots.
- [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches.
- [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand.
- [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance.
- [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows.
- [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses.
- [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research.
- [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction.
- [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation.
- [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Codereadr MCP?

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

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

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

---
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