College football data with OpenAI Agents SDK

How to integrate College football data MCP with OpenAI Agents SDK

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Introduction Also integrate College football data with TL;DR What is OpenAI Agents SDK What is the College football data MCP server Supported Tools & Triggers Creating MCP Server - Stand-alone vs Composio SDK Step-by-step Guide Complete Code Conclusion How to build College football data MCP Agent with another framework Explore Other Toolkits FAQ

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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 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.

Also integrate College football data with

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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 OpenAI 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 ScoreRetrieves advanced analytics for a single college football game including: - Team metrics: PPA (Predicted Points Added), success rates, rushing efficiency, havoc rates, scoring opportunities - Player metrics: Usage rates by quarter and play type, individual PPA breakdowns - Game info: Teams, scores, win probabilities, excitement index Requires a valid gameId from Get Games and Results action.

Advanced Game StatsTool to retrieve advanced team metrics at the game level.

Advanced Season Stats by TeamRetrieve advanced season-level team statistics including PPA (Predicted Points Added), success rates, explosiveness, havoc metrics, and rushing/passing efficiency breakdowns.

Betting LinesTool to fetch betting lines and totals by game and provider.

Composite Team TalentFetches 247Sports composite team talent rankings for a given season.

Conference MembershipsTool to retrieve current conference memberships for college football teams.

Divisions by ConferenceTool to list FBS/FCS conference divisions with active years and metadata.

Get Conference SP+ RatingsRetrieve aggregated historical conference SP+ (Success Rate + Points Per Play) ratings for college football conferences.

Get Drive DataRetrieves college football drive-level data including offensive/defensive teams, yards gained, drive results (TD, PUNT, INT, etc.

Get Field Goal Expected PointsRetrieves field goal expected points values for various field positions and distances.

FPI RatingsRetrieves historical Football Power Index (FPI) ratings for college football teams.

Get Game Havoc StatsTool to retrieve havoc statistics aggregated by game.

Get Game MediaRetrieve broadcast information for college football games including TV channels, streaming platforms, and radio outlets.

Get Games and ResultsTool to retrieve college American football games and results for a given season/week/team.

Get Player Game StatsFetches detailed player statistics for college football games.

Get Player UsageRetrieves player usage data for a given season.

Get Play TypesTool to fetch all available play types.

Get Predicted Points Added By TeamTool to retrieve historical team Predicted Points Added (PPA) metrics by season.

Get Pregame Win ProbabilitiesTool to retrieve pregame win probabilities for college football games.

Get RecruitsRetrieves player recruiting rankings from the College Football Data API.

Get Stats CategoriesTool to fetch all available team statistical categories.

Get Team Game StatsFetch team-level box score statistics for college football games.

Get Team Recruiting RankingsRetrieve team recruiting rankings from the College Football Data API.

Get Teams ATS RecordsTool to retrieve against-the-spread (ATS) summary by team.

Get User InfoRetrieves information about the authenticated user from the College Football Data API.

Get Win ProbabilityTool to query play-by-play win probabilities for a specific game.

List Coaches and HistoryTool to get coaching records and history.

List ConferencesRetrieves all college football conferences from the College Football Data API.

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 TeamsRetrieve a list of college football teams from the CFBD (College Football Data) API.

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 PositionsRetrieves the standardized list of 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 PlayerFetch player-level statistics tied to individual plays.

Play Stat TypesTool to fetch all play-level stat type definitions.

Player PPA by GameRetrieve player-level PPA (Predicted Points Added) / EPA (Expected Points Added) stats for individual games.

PPA Player By SeasonTool to fetch player-level PPA/EPA aggregated by season.

Predict Expected Points (EP)Get expected points (EP) for all field positions given a specific down and distance scenario.

PPA Team By GameTool to retrieve team Predicted Points Added (PPA) by game.

Rankings PollsRetrieve college football poll rankings (AP Top 25, Coaches Poll, Playoff Committee, FCS, Division II/III).

Elo RatingsTool to retrieve Elo ratings for college football teams.

SP+ RatingsRetrieve SP+ (Success Rate + Points Per Play) team ratings for college football.

SRS RatingsRetrieves Simple Rating System (SRS) team ratings.

Recruiting Group DictionaryRetrieves aggregated college football recruiting data grouped by position.

Recruiting Transfer PortalRetrieves NCAA college football transfer portal entries for a given season.

Returning Production by TeamTool to fetch Bill Connelly–style returning production splits by team and season.

Search PlayersSearch for college football players by name.

Season Stats PlayerFetch aggregated season statistics for college football players.

Season Team StatsTool to get basic season stats aggregated by team and season.

Season Types DictionaryRetrieve the list of available season types for a specific college football year.

Team Matchup HistoryTool to retrieve head-to-head team matchup records over a date range.

Get team season recordsRetrieve college football team win-loss records for a specific season.

Get Team RosterFetches the roster for a college football team for a specific season.

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

What is Composio SDK?

Composio's Composio SDK 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 Composio SDK

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

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
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

Log in to the Composio dashboard.
Go to Settings and copy your 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 OpenAI 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.

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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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Harsha GaddipatiCo-founder, Slashy

Karan skipped his own birthday party to fix our critical issue. It was 10 pm and he diverted his Waymo to help us instead. This really sets the bar, shows you the commitment you need to have when users rely on your software.

Abhi AryaCo-founder, Opennote

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it has immensely helped in keeping new users on the platform.

Nirman DaveCEO, Zams

We chose Composio over Pipedream because it delivered depth where it mattered. It supported niche tools and tricky edge cases that other platforms simply ignored. Giving us confidence to scale without compromising.

Ryan YuFounder, Extra Thursday

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down in the complexities of managing agent auth would have been a death sentence for Extra Thursday.

Tomisin JenrolaFounder & CEO, SwarmZero

Before partnering with Composio, adding tool integrations was a slow, resource-intensive process. Each integration could take weeks or months of engineering time, and maintaining them meant constantly keeping up with API changes.

Jerome LeclancheCo-Founder, Ingram Technologies

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Harsha GaddipatiCo-founder, Slashy

Abhi AryaCo-founder, Opennote

Nirman DaveCEO, Zams

Ryan YuFounder, Extra Thursday

Tomisin JenrolaFounder & CEO, SwarmZero

Jerome LeclancheCo-Founder, Ingram Technologies

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Harsha GaddipatiCo-founder, Slashy

Abhi AryaCo-founder, Opennote

Nirman DaveCEO, Zams

Ryan YuFounder, Extra Thursday

Tomisin JenrolaFounder & CEO, SwarmZero

Jerome LeclancheCo-Founder, Ingram Technologies

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

How to integrate College football data MCP with OpenAI Agents SDK

Table of Contents

Connect College football data without Auth hassles

Introduction

Also integrate College football data with

TL;DR

What is OpenAI Agents SDK?

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

Supported Tools & Triggers

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies

Set up the Composio instance

Create a Tool Router session

Configure the agent

Start chat loop and handle conversation

Complete Code

Conclusion

How to build College football data MCP Agent with another framework

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Explore Other Toolkits

Excel

21risk

Abstract

FAQ

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

Can I use Tool Router MCP with OpenAI Agents SDK?

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

How safe is my data with Composio Tool Router?

Used by agents from

Never worry about agent reliability