How to integrate College football data MCP with OpenAI Agents SDK

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Introduction

This guide walks you through connecting College football data to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working College football data agent that can show betting lines for this week's games, get tv schedule for sec games this weekend, list advanced box scores for ohio state, summarize team talent rankings for 2024 through natural language commands.

This guide will help you understand how to give your OpenAI Agents SDK agent real control over a College football data account through Composio's College football data 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 and set up your OpenAI and Composio API keys
  • Install the necessary dependencies
  • Initialize Composio and create a Tool Router session for College football data
  • Configure an AI agent that can use College football data as a tool
  • Run a live chat session where you can ask the agent to perform College football data operations

What is open-ai-agents-sdk?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.

Key features include:

  • Hosted MCP Tools: Connect to external services through hosted MCP endpoints
  • SQLite Sessions: Persist conversation history across interactions
  • Simple API: Clean interface with Agent, Runner, and tool configuration
  • Streaming Support: Real-time response streaming for interactive applications

What is the College football data MCP server, and what's possible with it?

The College football data MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your College Football Data account. It provides structured and secure access to comprehensive college football stats, schedules, advanced analytics, and recruiting data, so your agent can fetch game results, analyze team performance, retrieve broadcast info, and explore historical metrics on your behalf.

  • Retrieve game schedules and results: Instantly fetch upcoming games, past scores, and matchup outcomes filtered by season, week, team, or conference.
  • Analyze advanced team and player stats: Have your agent pull in-depth box scores, advanced metrics, and season-long analytics to compare team or player performance.
  • Access media and broadcast information: Quickly get details on TV, radio, and streaming coverage for selected games, including broadcast schedules and platforms.
  • Review team talent and recruiting rankings: Let your agent track composite team talent scores and recruiting class data across seasons for any program.
  • Explore historical conference and division data: Effortlessly trace a team's conference membership history, division alignment, and related metadata over time.

Supported Tools & Triggers

Tools
Advanced Box ScoreTool to retrieve advanced box score metrics for a single college football game.
Advanced Game StatsTool to retrieve advanced team metrics at the game level.
Advanced Season Stats by TeamTool to retrieve advanced season metrics aggregated by team and season.
Betting LinesTool to fetch betting lines and totals by game and provider.
Composite Team TalentTool to fetch composite team talent rankings by season.
Conference Memberships HistoryTool to retrieve historical conference memberships for teams, including years active and division.
Divisions by ConferenceTool to list FBS/FCS conference divisions with active years and metadata.
Get Drive DataTool to retrieve drive-level data and results.
Get Game MediaTool to retrieve game media information and broadcast schedules (TV, radio, web, etc.
Get Games and ResultsTool to retrieve games and results for a given season/week/team.
Get Player Game StatsTool to fetch player statistics at the game level.
Get Team Game StatsTool to fetch team statistics at the game level.
List Coaches and HistoryTool to get coaching records and history.
List ConferencesTool to list all college football conferences.
List FBS TeamsTool to list FBS teams for a given season.
List FCS TeamsTool to list FCS teams for a given season and conference.
List TeamsTool to list college football teams.
List Venues and StadiumsTool to list college football venues with metadata (name, capacity, location, etc.
NFL Draft PicksTool to list NFL Draft picks.
NFL Draft PositionsTool to list NFL draft positions.
NFL Draft TeamsTool to list NFL teams used in draft endpoints.
Play-by-Play DataTool to fetch play-by-play data for college football games.
Play Stats PlayerTool to fetch player-level stats tied to individual plays.
Play Stat TypesTool to fetch all play-level stat type definitions.
Player PPA by GameTool to retrieve player-level PPA/EPA broken down by game.
PPA Player By SeasonTool to fetch player-level PPA/EPA aggregated by season.
Predict Expected Points (EP)Tool to get expected points by down, distance, and field position.
PPA Team By GameTool to retrieve team Predicted Points Added (PPA) by game.
Rankings PollsTool to retrieve weekly human/computer poll rankings.
Elo RatingsTool to retrieve Elo ratings for college football teams.
SP+ RatingsTool to retrieve SP+ team ratings.
SRS RatingsTool to retrieve Simple Rating System (SRS) team ratings.
Recruiting Group DictionaryTool to list recruiting position group aggregations.
Recruiting Transfer PortalTool to retrieve transfer portal entries for a given season.
Returning Production by TeamTool to fetch Bill Connelly–style returning production splits by team and season.
Season Stats PlayerTool to fetch basic season stats aggregated by player and season.
Season Team StatsTool to get basic season stats aggregated by team and season.
Season Types DictionaryTool to list season types.
Team Matchup HistoryTool to retrieve head-to-head team matchup records over a date range.
Team season recordsTool to fetch team season records by year with optional filters.
Get Team RosterTool to fetch roster for a given team and season.

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:
  • Composio API Key and OpenAI API Key
  • Primary know-how of OpenAI Agents SDK
  • A live College football data project
  • Some knowledge of Python or Typescript

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

Install dependencies

pip install composio_openai_agents openai-agents python-dotenv

Install the Composio SDK and the OpenAI Agents SDK.

Set up environment variables

bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com

Create a .env file and add your OpenAI and Composio API keys.

Import dependencies

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
What's happening:
  • You're importing all necessary libraries.
  • The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like College football data.

Set up the Composio instance

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
What's happening:
  • load_dotenv() loads your .env file so OPENAI_API_KEY and COMPOSIO_API_KEY are available as environment variables.
  • Creating a Composio instance using the API Key and OpenAIAgentsProvider class.

Create a Tool Router session

# Create a College football data Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["college_football_data"]
)

mcp_url = session.mcp.url

What is happening:

  • You give the Tool Router the user id and the toolkits you want available. Here, it is only college_football_data.
  • The router checks the user's College football data connection and prepares the MCP endpoint.
  • The returned session.mcp.url is the MCP URL that your agent will use to access College football data.
  • This approach keeps things lightweight and lets the agent request College football data tools only when needed during the conversation.

Configure the agent

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access College football data. "
        "Help users perform College football data operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
What's happening:
  • We're creating an Agent instance with a name, model (gpt-5), and clear instructions about its purpose.
  • The agent's instructions tell it that it can access College football data and help with queries, inserts, updates, authentication, and fetching database information.
  • The tools array includes a HostedMCPTool that connects to the MCP server URL we created earlier.
  • The headers dict includes the Composio API key for secure authentication with the MCP server.
  • require_approval: 'never' means the agent can execute College football data operations without asking for permission each time, making interactions smoother.

Start chat loop and handle conversation

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
What's happening:
  • The program prints a session URL that you visit to authorize College football data.
  • After authorization, the chat begins.
  • Each message you type is processed by the agent using Runner.run().
  • The responses are printed to the console, and conversations are saved locally using SQLite.
  • Typing exit, quit, or q cleanly ends the chat.

Complete Code

Here's the complete code to get you started with College football data and open-ai-agents-sdk:

import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["college_football_data"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access College football data. "
        "Help users perform College football data operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())

Conclusion

This was a starter code for integrating College football data MCP with OpenAI Agents SDK to build a functional AI agent that can interact with College football data.

Key features:

  • Hosted MCP tool integration through Composio's Tool Router
  • SQLite session persistence for conversation history
  • Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

How to build College football data MCP Agent with another framework

Claude Agent SDK

Claude Agent SDK

Use College football data MCP with Claude Agent SDK

Google ADK

Google ADK

Use College football data MCP with Google ADK

LangChain

LangChain

Use College football data MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use College football data MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use College football data MCP with Mastra AI

LlamaIndex

LlamaIndex

Use College football data MCP with LlamaIndex

CrewAI

CrewAI

Use College football data MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and College football data MCP?

With a standalone College football data MCP server, the agents and LLMs can only access a fixed set of College football data tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from College football data and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK 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 College football data tools.

Can I manage the permissions and scopes for College football data while using Tool Router?

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

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