# How to integrate Rosette text analytics MCP with CrewAI

```json
{
  "title": "How to integrate Rosette text analytics MCP with CrewAI",
  "toolkit": "Rosette text analytics",
  "toolkit_slug": "rosette_text_analytics",
  "framework": "CrewAI",
  "framework_slug": "crew-ai",
  "url": "https://composio.dev/toolkits/rosette_text_analytics/framework/crew-ai",
  "markdown_url": "https://composio.dev/toolkits/rosette_text_analytics/framework/crew-ai.md",
  "updated_at": "2026-10-12T12:45:17.003Z"
}
```

## Introduction

This guide walks you through connecting Rosette text analytics to CrewAI using the Composio tool router. By the end, you'll have a working Rosette text analytics agent that can detect language of this customer review, compare these two addresses for similarity, check if these organization names match through natural language commands.
This guide will help you understand how to give your CrewAI agent real control over a Rosette text analytics account through Composio's Rosette text analytics MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Rosette text analytics with

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

## TL;DR

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

The Rosette text analytics MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Rosette text analytics account. It provides structured and secure access to Rosette's powerful text analysis features, so your agent can identify languages, compare addresses, and evaluate name similarities on your behalf.
- Automatic language identification: Instantly detect the natural language of any given text, including confidence scores and genre-specific analysis.
- Address similarity scoring: Compare two addresses—single strings or structured objects—and receive a similarity score to help with deduplication or data matching.
- Name similarity comparison: Evaluate how similar two names (person, organization, or location) are, returning a score for identity resolution or record linkage.
- Multilingual and cross-script support: Analyze and process text across hundreds of language, encoding, and script combinations without manual intervention.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ROSETTE_TEXT_ANALYTICS_ADDRESS_SIMILARITY` | Address Similarity | Compares two addresses and returns a similarity score. Addresses can be provided as single strings or as structured objects. The tool is optimized for English, Simplified Chinese, and Traditional Chinese addresses. |
| `ROSETTE_TEXT_ANALYTICS_LANGUAGE_IDENTIFICATION` | Identify Language | This tool identifies the natural language of a given text. It takes a string of text as input and returns the detected language along with a confidence score. Optional parameters include specifying a genre (e.g., "social-media"), providing a list of language codes to constrain the identification, and indicating whether to include user-defined languages. |
| `ROSETTE_TEXT_ANALYTICS_NAME_SIMILARITY` | Compare Name Similarity | The 'Name Similarity' tool compares two entity names (Person, Location, or Organization) and returns a similarity score between 0 and 1 to indicate if the names are similar. It is useful for tasks such as record linkage, identity resolution, and data deduplication. |

## Supported Triggers

None listed.

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

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

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

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=["rosette_text_analytics"],
)
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 Rosette text analytics through Composio's Tool Router. The agent can perform Rosette text analytics 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 Rosette text analytics MCP Agent with another framework

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

## Related Toolkits

- [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.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows.
- [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application.
- [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities.
- [AltTextLab](https://composio.dev/toolkits/alt_text_lab) - AltTextLab is an AI service that generates accessible, SEO-aware alt text for images. It helps teams make image content easier to understand, index, and publish.
- [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization.
- [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications.
- [Apiframe](https://composio.dev/toolkits/apiframe) - Apiframe is an API platform for generating and managing AI images, videos, music, uploads, assets, jobs, and custom LoRAs. It gives developers one clean API for creative AI workflows without juggling multiple model providers.
- [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations.
- [Arize AX](https://composio.dev/toolkits/arize_ax) - Arize AX is an AI engineering platform for tracing, evaluating, and improving AI applications. Use it to debug LLM behavior, compare experiments, and improve production AI quality.
- [Artificial Analysis](https://composio.dev/toolkits/artificial_analysis) - Artificial Analysis is an independent benchmarking platform for AI models and API providers. Use it to compare model intelligence, coding, math, pricing, latency, throughput, and arena rankings.
- [AssemblyAI](https://composio.dev/toolkits/assemblyai) - AssemblyAI is a speech-to-text and audio intelligence API for uploaded or hosted media. Use it to turn audio and video into accurate transcripts, summaries, and insights.
- [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key.
- [Avoma](https://composio.dev/toolkits/avoma) - Avoma is an AI meeting assistant for recording, transcribing, analyzing, and managing meetings, calls, notes, scorecards, and conversation intelligence. It helps teams turn conversations into searchable notes, follow-ups, coaching insights, and revenue intelligence.
- [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient.
- [Black Forest Labs](https://composio.dev/toolkits/bfl) - Black Forest Labs provides hosted FLUX models for generating and editing images and videos. It offers fast, flexible media creation with asynchronous processing and fine-tuning support.
- [Bland AI](https://composio.dev/toolkits/bland_ai) - Bland AI is a conversational AI platform for building voice agents, phone calls, messaging, and communication workflows. Use it to automate calling, evaluate conversations, and run voice operations at scale.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Rosette text analytics MCP?

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

### Can I manage the permissions and scopes for Rosette text analytics while using Tool Router?

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

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