How to integrate College football data MCP with Pydantic AI

This guide walks you through connecting College football data to Pydantic AI 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 Pydantic AI 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.

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College football data

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College football data delivers comprehensive NCAA football stats, scores, and recruiting details via API. Get real-time, historical, and advanced analytics for teams, games, and players.

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Introduction

This guide will help you understand how to give your Pydantic AI 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:

How to set up your Composio API key and User ID
How to create a Composio Tool Router session for College football data
How to attach an MCP Server to a Pydantic AI agent
How to stream responses and maintain chat history
How to build a simple REPL-style chat interface to test your College football data workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

Type Safety: Built on Pydantic for automatic data validation
MCP Support: Native support for Model Context Protocol servers
Streaming: Built-in support for streaming responses
Async First: Designed for async/await patterns

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.

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

Step by step09 STEPS

Prerequisites

Before starting, make sure you have:

Python 3.9 or higher
A Composio account with an active API key
Basic familiarity with Python and async programming

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 pydantic-ai python-dotenv

Install the required libraries.

What's happening:

composio connects your agent to external SaaS tools like College football data
pydantic-ai lets you create structured AI agents with tool support
python-dotenv loads your environment variables securely from a .env file

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

Create a .env file in your project root.

What's happening:

COMPOSIO_API_KEY authenticates your agent to Composio's API
USER_ID associates your session with your account for secure tool access
OPENAI_API_KEY to access OpenAI LLMs

Import dependencies

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

What's happening:

We load environment variables and import required modules
Composio manages connections to College football data
MCPServerStreamableHTTP connects to the College football data MCP server endpoint
Agent from Pydantic AI lets you define and run the AI assistant

Create a Tool Router Session

python

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for College football data
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["college_football_data"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

What's happening:

We're creating a Tool Router session that gives your agent access to College football data tools
The create method takes the user ID and specifies which toolkits should be available
The returned session.mcp.url is the MCP server URL that your agent will use

Initialize the Pydantic AI Agent

python

# Attach the MCP server to a Pydantic AI Agent
college_football_data_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[college_football_data_mcp],
    instructions=(
        "You are a College football data assistant. Use College football data tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)

What's happening:

The MCP client connects to the College football data endpoint
The agent uses GPT-5 to interpret user commands and perform College football data operations
The instructions field defines the agent's role and behavior

Build the chat interface

python

# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with College football data.\n")

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", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()

What's happening:

The agent reads input from the terminal and streams its response
College football data API calls happen automatically under the hood
The model keeps conversation history to maintain context across turns

Run the application

python

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

What's happening:

The asyncio loop launches the agent and keeps it running until you exit

Complete Code

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

python

import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for College football data
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["college_football_data"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    college_football_data_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[college_football_data_mcp],
        instructions=(
            "You are a College football data assistant. Use College football data tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with College football data.\n")

    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", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

Conclusion

You've built a Pydantic AI agent that can interact with College football data through Composio's Tool Router. With this setup, your agent can perform real College football data actions through natural language. You can extend this further by:

Adding other toolkits like Gmail, HubSpot, or Salesforce
Building a web-based chat interface around this agent
Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + College football data for workflow automation)

This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

TOOLS

Supported Tools

Every College football data action and event your agent gets out of the box.

Advanced Box Score

Retrieves 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 Stats

Tool to retrieve advanced team metrics at the game level.

Advanced Season Stats by Team

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

Betting Lines

Tool to fetch betting lines and totals by game and provider.

Composite Team Talent

Fetches 247Sports composite team talent rankings for a given season.

Conference Memberships

Tool to retrieve current conference memberships for college football teams.

Divisions by Conference

Tool to list FBS/FCS conference divisions with active years and metadata.

Get Conference SP+ Ratings

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

Get Drive Data

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

Get Field Goal Expected Points

Retrieves field goal expected points values for various field positions and distances.

FPI Ratings

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

Get Game Havoc Stats

Tool to retrieve havoc statistics aggregated by game.

Get Game Media

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

Get Games and Results

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

Get Player Game Stats

Fetches detailed player statistics for college football games.

Get Player Usage

Retrieves player usage data for a given season.

Get Play Types

Tool to fetch all available play types.

Get Predicted Points Added By Team

Tool to retrieve historical team Predicted Points Added (PPA) metrics by season.

Get Pregame Win Probabilities

Tool to retrieve pregame win probabilities for college football games.

Get Recruits

Retrieves player recruiting rankings from the College Football Data API.

Get Stats Categories

Tool to fetch all available team statistical categories.

Get Team Game Stats

Fetch team-level box score statistics for college football games.

Get Team Recruiting Rankings

Retrieve team recruiting rankings from the College Football Data API.

Get Teams ATS Records

Tool to retrieve against-the-spread (ATS) summary by team.

Get User Info

Retrieves information about the authenticated user from the College Football Data API.

Get Win Probability

Tool to query play-by-play win probabilities for a specific game.

List Coaches and History

Tool to get coaching records and history.

List Conferences

Retrieves all college football conferences from the College Football Data API.

List FBS Teams

Tool to list FBS teams for a given season.

List FCS Teams

Tool to list FCS teams for a given season and conference.

List Teams

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

List Venues and Stadiums

Tool to list college football venues with metadata (name, capacity, location, etc.

NFL Draft Picks

Tool to list NFL Draft picks.

NFL Draft Positions

Retrieves the standardized list of NFL draft positions.

NFL Draft Teams

Tool to list NFL teams used in draft endpoints.

Play-by-Play Data

Tool to fetch play-by-play data for college football games.

Play Stats Player

Fetch player-level statistics tied to individual plays.

Play Stat Types

Tool to fetch all play-level stat type definitions.

Player PPA by Game

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

PPA Player By Season

Tool 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 Game

Tool to retrieve team Predicted Points Added (PPA) by game.

Rankings Polls

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

Elo Ratings

Tool to retrieve Elo ratings for college football teams.

SP+ Ratings

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

SRS Ratings

Retrieves Simple Rating System (SRS) team ratings.

Recruiting Group Dictionary

Retrieves aggregated college football recruiting data grouped by position.

Recruiting Transfer Portal

Retrieves NCAA college football transfer portal entries for a given season.

Returning Production by Team

Tool to fetch Bill Connelly–style returning production splits by team and season.

Search Players

Search for college football players by name.

Season Stats Player

Fetch aggregated season statistics for college football players.

Season Team Stats

Tool to get basic season stats aggregated by team and season.

Season Types Dictionary

Retrieve the list of available season types for a specific college football year.

Team Matchup History

Tool to retrieve head-to-head team matchup records over a date range.

Get team season records

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

Get Team Roster

Fetches the roster for a college football team for a specific season.

FRAMEWORKS

How to build College football data MCP Agent with another framework

OpenAI Agents SDK

Use College football data MCP with OpenAI Agents SDK

Claude Agent SDK

Use College football data MCP with Claude Agent SDK

Claude Code

Use College football data MCP with Claude Code

Claude Cowork

Use College football data MCP with Claude Cowork

Codex

Use College football data MCP with Codex

OpenClaw

Use College football data MCP with OpenClaw

Hermes

Use College football data MCP with Hermes

CLI

Use College football data MCP with CLI

Google ADK

Use College football data MCP with Google ADK

LangChain

Use College football data MCP with LangChain

Vercel AI SDK

Use College football data MCP with Vercel AI SDK

Mastra AI

Use College football data MCP with Mastra AI

LlamaIndex

Use College football data MCP with LlamaIndex

CrewAI

Use College football data MCP with CrewAI

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FAQ

Frequently asked questions

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.

Yes, you can. Pydantic AI 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.

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.

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.

Start with College football data.It takes 30 seconds.

Managed auth, hosted MCP servers, and every College football data tool your agent needs.Free to start.

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Customer stories

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

Harsha Gaddipati

Co-founder, Slashy

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's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

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

Nirman Dave

CEO, Zams

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

Ryan Yu

Founder, Extra Thursday

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

Tomisin Jenrola

Founder & CEO, SwarmZero

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.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate College football data MCP with Pydantic AI

Connect College football data without auth hassles

Introduction

Also integrate College football data with

TL;DR

What is Pydantic AI?

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

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

Create a Tool Router Session

Initialize the Pydantic AI Agent

Build the chat interface

Run the application

Complete Code

Conclusion

Supported Tools

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

Addressfinder

Frequently asked questions

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

Can I use Tool Router MCP with Pydantic AI?+

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

How safe is my data with Composio Tool Router?+

Start with College football data.It takes 30 seconds.

Teams ship real agents on Composio