College football data with AutoGen

How to integrate College football data MCP with Autogen

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Introduction Also integrate College football data with TL;DR What is AutoGen 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 AutoGen 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 AutoGen 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

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

Get and set up your OpenAI and Composio API keys
Install the required dependencies for Autogen and Composio
Initialize Composio and create a Tool Router session for College football data
Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
Configure an Autogen AssistantAgent that can call College football data tools
Run a live chat loop where you ask the agent to perform College football data operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

Multi-Agent Systems: Build collaborative agent workflows
MCP Workbench: Native support for Model Context Protocol tools
Streaming HTTP: Connect to external services through streamable HTTP
AssistantAgent: Pre-built agent class for tool-using assistants

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

You will need:

A Composio API key
An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
A College football data account you can connect to Composio
Some basic familiarity with Autogen and Python async

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 python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

composio connects your agent to College football data via MCP
autogen-agentchat provides the AssistantAgent class
autogen-ext-openai provides the OpenAI model client
autogen-ext-tools provides MCP workbench support

Set up environment variables

bash

COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

COMPOSIO_API_KEY is required to talk to Composio
OPENAI_API_KEY is used by Autogen's OpenAI client
USER_ID is how Composio identifies which user's College football data connections to use

Import dependencies and create Tool Router session

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a College football data session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["college_football_data"]
    )
    url = session.mcp.url

What's happening:

load_dotenv() reads your .env file
Composio(api_key=...) initializes the SDK
create(...) creates a Tool Router session that exposes College football data tools
session.mcp.url is the MCP endpoint that Autogen will connect to

Configure MCP parameters for Autogen

python

# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

url points to the Tool Router MCP endpoint from Composio
timeout is the HTTP timeout for requests
sse_read_timeout controls how long to wait when streaming responses
terminate_on_close=True cleans up the MCP server process when the workbench is closed

Create the model client and agent

python

# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create College football data assistant agent with MCP tools
    agent = AssistantAgent(
        name="college_football_data_assistant",
        description="An AI assistant that helps with College football data operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

OpenAIChatCompletionClient wraps the OpenAI model for Autogen
McpWorkbench connects the agent to the MCP tools
AssistantAgent is configured with the College football data tools from the workbench

Run the interactive chat loop

python

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any College football data related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

What's happening:

The script prompts you in a loop with You:
Autogen passes your input to the model, which decides which College football data tools to call via MCP
agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with College football data and AutoGen:

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a College football data session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["college_football_data"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create College football data assistant agent with MCP tools
        agent = AssistantAgent(
            name="college_football_data_assistant",
            description="An AI assistant that helps with College football data operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any College football data related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into College football data through Composio's Tool Router and MCP. From here you can:

Add more toolkits to the toolkits list, for example notion or hubspot
Refine the agent description to point it at specific workflows
Wrap this script behind a UI, Slack bot, or internal tool

Once the pattern is clear for College football data, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

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TOOLKIT MARKETPLACE

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 Autogen?

Yes, you can. Autogen 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.

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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 Autogen

Table of Contents

Connect College football data without Auth hassles

Introduction

Also integrate College football data with

TL;DR

What is AutoGen?

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 and create Tool Router session

Configure MCP parameters for Autogen

Create the model client and agent

Run the interactive chat loop

Complete Code

Conclusion

How to build College football data MCP Agent with another framework

OpenAI Agents SDK

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 Autogen?

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