How to integrate College football data MCP with LangChain

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Introduction

This guide walks you through connecting College football data to LangChain 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 LangChain 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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TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your College football data project to Composio
  • Create a Tool Router MCP session for College football data
  • Initialize an MCP client and retrieve College football data tools
  • Build a LangChain agent that can interact with College football data
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI 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

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_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 your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with College football data functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to College football data tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for College football data
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['college_football_data']
)

url = session.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
  • This approach allows the agent to dynamically load and use College football data tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "college_football_data-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our College football data MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available College football data tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

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")

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

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

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

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

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['college_football_data']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "college_football_data-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    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")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

Conclusion

You've successfully built a LangChain agent that can interact with College football data through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

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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 LangChain?

Yes, you can. LangChain 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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