How to integrate The odds api MCP with CrewAI

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Introduction

This guide walks you through connecting The odds api to CrewAI using the Composio tool router. By the end, you'll have a working The odds api agent that can show live nba game odds right now, list upcoming soccer matches this weekend, get current scores for mlb games, find top bookmakers for next nfl event through natural language commands.

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

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

TL;DR

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

The The odds api MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your The odds api account. It provides structured and secure access to real-time sports betting odds and event data, so your agent can look up live odds, fetch sports schedules, track scores, and analyze betting markets worldwide on your behalf.

  • Live odds retrieval for events: Instantly fetch up-to-date betting odds for specific games or matches across multiple bookmakers and markets.
  • Comprehensive sports and event discovery: Ask your agent to list current and upcoming sporting events, including which sports are in season and event-specific details.
  • Participant and team lookups: Easily get information about teams, players, or participants involved in any listed sport to inform your analysis or predictions.
  • Live and recent score tracking: Stay up to date with real-time scores and recently completed game results for your favorite sports.
  • Regional and market-based odds filtering: Compare odds for specific regions, bookmakers, or betting markets to find the best opportunities.

Supported Tools & Triggers

Tools
Get Event OddsTool to retrieve odds for a specific event.
Get EventsTool to fetch live and upcoming events for a specified sport.
Get OddsTool to fetch live and upcoming event odds for a specified sport, including bookmakers, regions, and markets.
Get ParticipantsTool to fetch list of participants (teams or players) for a specified sport.
Get ScoresTool to return live and recently completed event scores for a sport.
Get SportsTool to retrieve a list of in-season sports.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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 The odds api 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 python-dotenv
What's happening:
  • composio connects your agent to The odds api via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools 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
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
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 The odds api MCP URL

Create a Composio Tool Router session for The odds api

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["the_odds_api"],
)
url = session.mcp.url
What's happening:
  • You create a The odds api only session through Composio
  • Composio returns an MCP HTTP URL that exposes The odds api tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="The odds api Assistant",
    goal="Help users interact with The odds api through natural language commands",
    backstory=(
        "You are an expert assistant with access to The odds api tools. "
        "You can perform various The odds api operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the The odds api MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform The odds api operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their The odds api related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_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:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_the_odds_api_agent.py

Complete Code

Here's the complete code to get you started with The odds api and CrewAI:

python
# file: crewai_the_odds_api_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

def main():
    # Initialize Composio and create a The odds api session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["the_odds_api"],
    )
    url = session.mcp.url

    # Configure LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create The odds api assistant agent
    toolkit_agent = Agent(
        role="The odds api Assistant",
        goal="Help users interact with The odds api through natural language commands",
        backstory=(
            "You are an expert assistant with access to The odds api tools. "
            "You can perform various The odds api operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform The odds api operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their The odds api related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to The odds api through Composio's Tool Router. The agent can perform The odds api 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 The odds api MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use The odds api MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use The odds api MCP with Claude Agent SDK

Google ADK

Google ADK

Use The odds api MCP with Google ADK

LangChain

LangChain

Use The odds api MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use The odds api MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use The odds api MCP with Mastra AI

LlamaIndex

LlamaIndex

Use The odds api MCP with LlamaIndex

FAQ

What are the differences in Tool Router MCP and The odds api MCP?

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

Can I manage the permissions and scopes for The odds api while using Tool Router?

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

Used by agents from

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Context
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HubSpot
Agent.ai
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Entelligence
Rolai

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