CFBD API

cfbd-mcp-server: College Football Data MCP Server

What is cfbd-mcp-server?

The cfbd-mcp-server is a Model Context Protocol (MCP) server that provides AI assistants and applications, specifically Claude Desktop, with access to comprehensive college football statistics and data sourced from the College Football Data API V2. It allows users to query college football data using natural language.

How to use cfbd-mcp-server?

To use cfbd-mcp-server, you need Python 3.11 or higher, the UV package manager, and a College Football Data API key. Installation can be done automatically via Smithery using npx -y @smithery/cli install cfbd --client claude, or manually by cloning the repository, setting up a virtual environment, installing dependencies (uv pip install -e .), and adding your API key to a .env file. After installation, you run the server using uv run cfbd-mcp-server and configure Claude Desktop by adding the server details to its claude_desktop_config.json file. Updates involve pulling the latest code, uninstalling and reinstalling the package, and restarting Claude Desktop.

Key Features of cfbd-mcp-server

• Comprehensive Data Access: Query college football statistics, game results, team records, player statistics, play-by-play data, drive summaries, rankings, and win probability metrics.
• Natural Language Queries: Users can run queries using natural language within AI assistants like Claude Desktop.
• Resources (Schema Documentation): Access schema documentation for various endpoints including games, records, plays, drives, rankings, and advanced box scores.
• Tools (Direct Endpoint Queries): Directly query endpoints such as get-games, get-records, get-plays, get-rankings, and get-advanced-box-score.
• Prompts (Pre-built Analysis Templates): Utilize pre-built analysis templates like analyze-game, analyze-team, analyze-trends, compare-teams, and analyze-rivalry.
• MIT License: The project is open-source under the MIT License.

Use Cases of cfbd-mcp-server

• Sports Analytics: Analyze college football game data, team performance, and player statistics.
• AI Assistant Integration: Enable AI assistants (e.g., Claude Desktop) to provide detailed college football insights in response to natural language queries.
• Research and Reporting: Generate reports and conduct research on college football trends and historical data.
• Fantasy Sports: Inform decisions for fantasy college football leagues by accessing detailed statistics.

FAQ from cfbd-mcp-server

Q: What are the prerequisites for running cfbd-mcp-server? A: Python 3.11 or higher, UV package manager, and a College Football Data API key.

Q: How do I get a College Football Data API key? A: You can obtain one from collegefootballdata.com/key.

Q: What are the API limits? A: The College Football Data API has a free tier with limited requests per minute. Patreon subscribers get higher rate limits. Efficient querying and handling rate limit errors are recommended.

Q: How do I troubleshoot API Key Errors? A: Verify your API key is correctly set in both the .env file and claude_desktop_config.json, and ensure the key is valid on the College Football Data website.

Q: What should I do if I encounter rate limiting? A: Space out your requests, consider a Patreon subscription for higher limits, and implement caching for frequently accessed data.

Q: Where can I get help if I have issues? A: You can open an issue on GitHub, review the API documentation, or check the College Football Data Discord.

College Football Data MCP Server

An MCP server implementation providing access to college football statistics sourced from the College Football Data API V2 within Claude Desktop.

Overview

This Model Context Protocol (MCP) server enables AI assistants and applications to:

• Query comprehensive college football statistics and data
• Access game results, team records, and player statistics
• Analyze play-by-play data and drive summaries
• View rankings and win probability metrics
• Compare team performances and generate insights

Users can run queries using natural language.

Sample output

Q. What was the largest upset among FCS games during the 2014 season?

A. Let me help find the biggest FCS upset from 2014. I'll look through the games data, focusing on games between FCS teams where there was a significant gap in team strength and an unexpected outcome.

The data suggests there were several notable upsets in FCS during 2014, but one that stands out was Eastern Washington defeating Washington 52-59 on September 6th, 2014. This was particularly significant because:

1. Washington was an FBS team from the Pac-12 conference
2. The game was played at Washington's home stadium (Husky Stadium)
3. Eastern Washington's win probability was only 1.4% according to the post-game stats
4. The 59 points Eastern Washington scored was the most ever by an FCS team against a Pac-12 opponent
5. Washington had been favored by multiple scores going into the game

Other notable FCS upsets from that season included:

• North Dakota State defeating Iowa State 34-14
• Bethune-Cookman defeating FIU 14-12
• Northern Iowa nearly upsetting Iowa (lost 31-23)

However, Eastern Washington over Washington appears to be the largest upset of the 2014 FCS season when considering:

• The difference in divisions (FCS vs FBS)
• The caliber of the FBS opponent (Power 5 conference)
• The scoring margin and offensive performance by the underdog
• The historical significance as one of the highest-scoring FCS wins over a Power 5 team
• The fact it happened on the road in a hostile environment

Prerequisites

• Python 3.11 or higher
• UV package manager (recommended)
• A College Football Data API key (get one here)

Installation

Installing via Smithery

To install College Football Data Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install cfbd --client claude

1. Clone this repository:

git clone https://github.com/yourusername/cfbd-mcp-server
cd cfbd-mcp-server

2. Create and activate a virtual environment:

uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

uv pip install -e .

4. Create a .env file in the project root and add your API key:

CFB_API_KEY=your_api_key_here

Manual Installation

1. Clone this repository:

git clone https://github.com/yourusername/cfbd-mcp-server
cd cfbd-mcp-server

2. Create and activate a virtual environment:

uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

uv pip install -e .

4. Create a .env file in the project root and add your API key:

CFB_API_KEY=your_api_key_here

Usage

Running the Server

Start the server:

uv run cfbd-mcp-server

Connecting with Claude Desktop

1. Open your Claude Desktop configuration at:

   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json

2. Add the server configuration:

{
    "mcpServers": {
        "cfbd-mcp-server": {
            "command": "uv",
            "args": [
                "--directory",
                "/full/path/to/cfbd-mcp-server",
                "run",
                "cfbd-mcp-server"
            ],
            "env": {
                "CFB_API_KEY": "xxx",
                "PATH": "/full/path/to/python"
            }
        }
    }
}

3. Close then restart Claude Desktop

Once you restart you can confirm the installation by clicking on the plus sign in the lower left corner of the text box. If successful you'll see "Add from cfbd-mcp-server" as one of the menu options. Here's an example screenshot.

Updating after install

1. Download the updated files

cd cfbd-mcp-server
git pull

2. Uninstall the existing package:

uv pip uninstall cfbd-mcp-server

3. Delete existing build artifacts and metadata

For Windows:

rmdir /s /q build dist
del /s /q *.egg-info

For macOS:

rm -rf build dist *.egg-info

4. Install the revised package and its dependencies

uv pip install -e .
uv sync --dev --all-extras

uv run cfbd-mcp-server

6. Close and restart Claude Desktop

Features

Resources

Access schema documentation for all endpoints:

• schema://games - Game information and scores
• schema://records - Team season records
• schema://games/teams - Detailed team game data
• schema://plays - Play-by-play information
• schema://drives - Drive summaries and results
• schema://play/stats - Individual play statistics
• schema://rankings - Team rankings across polls
• schema://metrics/wp/pregame - Pregame win probabilities
• schema://game/box/advanced - Advanced box score statistics

Tools

Query endpoints directly:

• get-games - Retrieve game data
• get-records - Get team records
• get-games-teams - Access team game statistics
• get-plays - Query play-by-play data
• get-drives - Analyze drive information
• get-play-stats - View play statistics
• get-rankings - Check team rankings
• get-pregame-win-probability - See win probabilities
• get-advanced-box-score - Access detailed game statistics and analytics

Prompts

Pre-built analysis templates:

• analyze-game - Get detailed analysis of a specific game
• analyze-team - Comprehensive single team analysis
• analyze-trends - Analyze trends over a season
• compare-teams - Compare performance of two teams
• analyze-rivalry - Analyze historical rivalry matchups

API Limits

The College Football Data API is free to use but has rate limiting:

• Free tier: Limited requests per minute
• CFBD Patreon subscribers get higher rate limits
• Use efficient querying patterns to avoid hitting limits
• Handle rate limit errors gracefully

Development

Project Structure

cfbd-mcp-server/
├── README.md
├── pyproject.toml
└── src/
    └── cfbd_mcp_server/
        ├── .env
        ├── __init__.py
        ├── cfbd_schema.py
        ├── schema_helpers.py
        └── server.py

Setting Up for Development

1. Clone the repository
2. Install development dependencies:

uv pip install -e ".[dev]"

3. Run tests:

pytest

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to your fork
5. Submit a pull request

Troubleshooting

Common Issues

1. API Key Errors

   • Verify your API key is correctly set in both the .env and claude_desktop_config.json files
   • Check the key is valid at collegefootballdata.com

2. Rate Limiting

   • Space out requests when possible
   • Consider Patreon subscription for higher limits
   • Implement caching for frequently accessed data

3. Connection Issues

   • Verify internet connectivity
   • Check API status at collegefootballdata.com
   • Ensure proper error handling in your code

Getting Help

• Open an issue on GitHub
• Review the API documentation
• Check the College Football Data Discord

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• College Football Data for providing the API
• Model Context Protocol for the MCP specification