How to integrate College football data MCP with CrewAI

This guide walks you through connecting College football data to CrewAI 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 CrewAI 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.

Get started for freeGet a demo
College football data logoCollege football data
Api Key

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.

56 Tools

Introduction

This guide walks you through connecting College football data to CrewAI 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 CrewAI 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 SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndex

TL;DR

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

  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

Step by step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A College football data connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
2

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.
3

Install dependencies

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to College football data via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
4

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
5

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
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 College football data MCP URL
6

Create a Composio Tool Router session for College football data

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["college_football_data"])

url = session.mcp.url
What's happening:
  • You create a College football data only session through Composio
  • Composio returns an MCP HTTP URL that exposes College football data tools
7

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
8

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\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"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[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:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

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

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["college_football_data"],
)
url = session.mcp.url

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

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\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"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

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

Conclusion

You now have a CrewAI agent connected to College football data through Composio's Tool Router. The agent can perform College football data 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
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 logo

OpenAI Agents SDK

Use College football data MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use College football data MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use College football data MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use College football data MCP with Claude Cowork

Codex logo

Codex

Use College football data MCP with Codex

OpenClaw logo

OpenClaw

Use College football data MCP with OpenClaw

Hermes logo

Hermes

Use College football data MCP with Hermes

CLI logo

CLI

Use College football data MCP with CLI

Google ADK logo

Google ADK

Use College football data MCP with Google ADK

LangChain logo

LangChain

Use College football data MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use College football data MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use College football data MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use College football data MCP with LlamaIndex

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. 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 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.

Start building
Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREEBOOK A DEMO
agent.aiZoomLettaGleanHubSpotAWSWabi

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
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
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
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
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
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
Jerome Leclanche
Co-Founder, Ingram Technologies