# How to integrate Token metrics MCP with CrewAI

```json
{
  "title": "How to integrate Token metrics MCP with CrewAI",
  "toolkit": "Token metrics",
  "toolkit_slug": "token_metrics",
  "framework": "CrewAI",
  "framework_slug": "crew-ai",
  "url": "https://composio.dev/toolkits/token_metrics/framework/crew-ai",
  "markdown_url": "https://composio.dev/toolkits/token_metrics/framework/crew-ai.md",
  "updated_at": "2026-05-12T10:28:51.420Z"
}
```

## Introduction

This guide walks you through connecting Token metrics to CrewAI using the Composio tool router. By the end, you'll have a working Token metrics agent that can show real-time price for ethereum, list top 10 tokens by market cap, get technical indicators for bitcoin hourly through natural language commands.
This guide will help you understand how to give your CrewAI agent real control over a Token metrics account through Composio's Token metrics MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Token metrics with

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

## TL;DR

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

The Token metrics MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Token Metrics account. It provides structured and secure access to real-time cryptocurrency data and analytics, so your agent can retrieve token prices, analyze market trends, surface technical indicators, and provide actionable trading signals on your behalf.
- Real-time price and market data: Instantly get up-to-date prices, trading volumes, and market caps for any supported cryptocurrency, enabling smart portfolio management and trading decisions.
- Comprehensive token listings: Fetch a paginated list of supported crypto tokens, including metadata like price, supply, and contract details, to stay on top of the ever-evolving market.
- On-demand technical analysis: Retrieve technical indicators for any token and interval, so your agent can offer in-depth charting and analysis to guide investment strategies.
- Automated trading signals: Access AI-powered crypto trading entry and exit signals, helping automate or optimize your trading strategies based on actionable insights.
- Market cap leaderboards: Easily surface the top cryptocurrencies by market capitalization to monitor market trends, discover new opportunities, or rebalance your holdings.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `TOKEN_METRICS_GET_PRICE` | Get Price | Tool to retrieve real-time price and market metrics for a given cryptocurrency. Use when you need the latest price, volume, and market cap information for trading or analysis. Response fields like `volume24h` or `numberOfHolders` may be absent; treat missing fields as null, not zero. |
| `TOKEN_METRICS_GET_TECHNICAL_INDICATORS` | Get Technical Indicators | Tool to retrieve technical indicators for a token. Use when you need technical analysis data for a specific symbol, interval, and indicator. |
| `TOKEN_METRICS_GET_TOKENS` | Get Tokens | Tool to retrieve a paginated list of supported tokens with metadata. Use when you need comprehensive token listings across price, market cap, supply, and contract details. Returns token_id values required by TOKEN_METRICS_GET_PRICE and other endpoints — build your token_id mapping here first. Response fields such as volume24h and numberOfHolders may be absent for some tokens; treat missing values as null/unknown, not zero. tokenCreationDate is ISO-8601; convert to UTC for accurate age comparisons. |
| `TOKEN_METRICS_GET_TOP_MARKET_CAP_TOKENS` | Get Top Market Cap Tokens | Tool to retrieve a list of tokens ranked by market capitalization. Use when you need an overview of the most valuable cryptocurrencies. |
| `TOKEN_METRICS_GET_TRADING_SIGNALS` | Get Trading Signals | Tool to retrieve entry and exit crypto trading signals. Use when optimizing trading strategies with signal-based insights. |

## Supported Triggers

None listed.

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

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

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

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

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

## Related Toolkits

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- [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.
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- [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.
- [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Token metrics MCP?

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

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

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

---
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