How to integrate College football data MCP with Vercel AI SDK v6

This guide walks you through connecting College football data to Vercel AI SDK v6 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 Vercel AI 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.

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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.

56 Tools

Introduction

This guide walks you through connecting College football data to Vercel AI SDK v6 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 Vercel AI 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.

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TL;DR

Here's what you'll learn:
  • How to set up and configure a Vercel AI SDK agent with College football data integration
  • Using Composio's Tool Router to dynamically load and access College football data tools
  • Creating an MCP client connection using HTTP transport
  • Building an interactive CLI chat interface with conversation history management
  • Handling tool calls and results within the Vercel AI SDK framework

What is Vercel AI SDK?

The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state.

Key features include:

  • streamText: Core function for streaming responses with real-time tool support
  • MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp
  • Step Counting: Control multi-step tool execution with stopWhen: stepCountIs()
  • OpenAI Provider: Native integration with OpenAI models

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 step09 STEPS
1

Prerequisites

Before you begin, make sure you have:
  • Node.js and npm installed
  • A Composio account with API key
  • An OpenAI API key
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 required dependencies

bash
npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv

First, install the necessary packages for your project.

What you're installing:

  • @ai-sdk/openai: Vercel AI SDK's OpenAI provider
  • @ai-sdk/mcp: MCP client for Vercel AI SDK
  • @composio/core: Composio SDK for tool integration
  • ai: Core Vercel AI SDK
  • dotenv: Environment variable management
4

Set up environment variables

bash
OPENAI_API_KEY=your_openai_api_key_here
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here

Create a .env file in your project root.

What's needed:

  • OPENAI_API_KEY: Your OpenAI API key for GPT model access
  • COMPOSIO_API_KEY: Your Composio API key for tool access
  • COMPOSIO_USER_ID: A unique identifier for the user session
5

Import required modules and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});
What's happening:
  • We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio
  • The dotenv/config import automatically loads environment variables
  • The MCP client import enables connection to Composio's tool server
6

Create Tool Router session and initialize MCP client

typescript
async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["college_football_data"],
  });

  const mcpUrl = 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 mcp object contains the URL and authentication headers needed to connect to the MCP server
  • This session provides access to all College football data-related tools through the MCP protocol
7

Connect to MCP server and retrieve tools

typescript
const mcpClient = await createMCPClient({
  transport: {
    type: "http",
    url: mcpUrl,
    headers: session.mcp.headers, // Authentication headers for the Composio MCP server
  },
});

const tools = await mcpClient.tools();
What's happening:
  • We're creating an MCP client that connects to our Composio Tool Router session via HTTP
  • The mcp.url provides the endpoint, and mcp.headers contains authentication credentials
  • The type: "http" is important - Composio requires HTTP transport
  • tools() retrieves all available College football data tools that the agent can use
8

Initialize conversation and CLI interface

typescript
let messages: ModelMessage[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log(
  "Ask any questions related to college_football_data, like summarize my last 5 emails, send an email, etc... :)))\n",
);

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();
What's happening:
  • We initialize an empty messages array to maintain conversation history
  • A readline interface is created to accept user input from the command line
  • Instructions are displayed to guide the user on how to interact with the agent
9

Handle user input and stream responses with real-time tool feedback

typescript
rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({ role: "user", content: trimmedInput });
  console.log("\nAgent is thinking...\n");

  try {
    const stream = streamText({
      model: openai("gpt-5"),
      messages,
      tools,
      toolChoice: "auto",
      stopWhen: stepCountIs(10),
      onStepFinish: (step) => {
        for (const toolCall of step.toolCalls) {
          console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • We use streamText instead of generateText to stream responses in real-time
  • toolChoice: "auto" allows the model to decide when to use College football data tools
  • stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations
  • onStepFinish callback displays which tools are being used in real-time
  • We iterate through the text stream to create a typewriter effect as the agent responds
  • The complete response is added to conversation history to maintain context
  • Errors are caught and displayed with helpful retry suggestions

Complete Code

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

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Composio } from "@composio/core";
import * as readline from "readline";
import { streamText, type ModelMessage, stepCountIs } from "ai";
import { createMCPClient } from "@ai-sdk/mcp";

const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!process.env.OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey,
});

async function main() {
  // Create a tool router session for the user
  const session = await composio.create(composioUserID!, {
    toolkits: ["college_football_data"],
  });

  const mcpUrl = session.mcp.url;

  const mcpClient = await createMCPClient({
    transport: {
      type: "http",
      url: mcpUrl,
      headers: session.mcp.headers, // Authentication headers for the Composio MCP server
    },
  });

  const tools = await mcpClient.tools();

  let messages: ModelMessage[] = [];

  console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
  console.log(
    "Ask any questions related to college_football_data, like summarize my last 5 emails, send an email, etc... :)))\n",
  );

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
      console.log("\nGoodbye!");
      rl.close();
      process.exit(0);
    }

    if (!trimmedInput) {
      rl.prompt();
      return;
    }

    messages.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    try {
      const stream = streamText({
        model: openai("gpt-5"),
        messages,
        tools,
        toolChoice: "auto",
        stopWhen: stepCountIs(10),
        onStepFinish: (step) => {
          for (const toolCall of step.toolCalls) {
            console.log(`[Using tool: ${toolCall.toolName}]`);
          }
          if (step.toolCalls.length > 0) {
            console.log(""); // Add space after tool calls
          }
        },
      });

      for await (const chunk of stream.textStream) {
        process.stdout.write(chunk);
      }

      console.log("\n\n---\n");

      // Get final result for message history
      const response = await stream.response;
      if (response?.messages?.length) {
        messages.push(...response.messages);
      }
    } catch (error) {
      console.error("\nAn error occurred while talking to the agent:");
      console.error(error);
      console.log(
        "\nYou can try again or restart the app if it keeps happening.\n",
      );
    } finally {
      rl.prompt();
    }
  });

  rl.on("close", async () => {
    await mcpClient.close();
    console.log("\n👋 Session ended.");
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});

Conclusion

You've successfully built a College football data agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback.

Key features of this implementation:

  • Real-time streaming responses for a better user experience with typewriter effect
  • Live tool execution feedback showing which tools are being used as the agent works
  • Dynamic tool loading through Composio's Tool Router with secure authentication
  • Multi-step tool execution with configurable step limits (up to 10 steps)
  • Comprehensive error handling for robust agent execution
  • Conversation history maintenance for context-aware responses

You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.
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

Mastra AI logo

Mastra AI

Use College football data MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use College football data MCP with LlamaIndex

CrewAI logo

CrewAI

Use College football data MCP with CrewAI

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. Vercel AI SDK v6 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.

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