College Football Data MCP Server

What is College Football Data MCP Server about?

A Model Context Protocol (MCP) server that interfaces with the College Football Data API (V2) and exposes a rich set of football‑related resources—games, records, play‑by‑play, drives, rankings, advanced box scores, etc.—so that Claude Desktop (and other MCP‑compatible assistants) can answer natural‑language queries about college football.

How to use College Football Data MCP Server?

1. Prerequisites – Python 3.11+, a UV package manager (recommended), and a valid College Football Data API key.
2. Installation – Clone the repository, create a virtual environment with uv venv, activate it, and install the package in editable mode: uv pip install -e ..
3. Configuration – Add CFB_API_KEY=your_api_key to a .env file in the project root.
4. Run the server – uv run cfbd-mcp-server.
5. Connect Claude Desktop – Add an entry to claude_desktop_config.json pointing to the server command (see README for the exact JSON snippet) and restart Claude Desktop. The server will appear as “Add from cfbd‑mcp‑server” in the UI.

Key features of College Football Data MCP Server

• Broad resource schema – schema://games, schema://records, schema://plays, schema://rankings, schema://metrics/wp/pregame, schema://game/box/advanced, etc.
• Ready‑made tools – get-games, get-records, get-plays, get-drives, get-rankings, get-pregame-win-probability, get-advanced-box-score.
• Pre‑built prompts – Templates for analyzing a single game, a team, season trends, head‑to‑head comparisons, and rivalries.
• Rate‑limit awareness – Documentation on free‑tier limits and Patreon‑enhanced limits, with guidance on caching and efficient queries.
• Easy updates – Simple git‑pull + reinstall workflow to keep the server current.

Use cases of College Football Data MCP Server

• Fantasy football research – Quickly retrieve player statistics and game outcomes to inform draft decisions.
• Sports journalism – Generate concise game recaps or trend analyses directly within Claude Desktop.
• Coaching analytics – Pull play‑by‑play and drive data to evaluate play‑calling tendencies.
• Educational projects – Teach data‑driven storytelling with real‑world football data.
• Fan engagement bots – Power chatbots that answer fan questions about historic upsets, rankings, or win probabilities.

FAQ from the College Football Data MCP Server

Q: Do I need a paid API plan? A: A free API key works, but it has stricter request limits. Patreon subscribers receive higher limits.

Q: Which Python version is required? A: Python 3.11 or newer.

Q: Can I run the server on Windows? A: Yes. Use uv venv and activate the virtual environment with .venv\Scripts\activate.

Q: How do I update the server after a new release? A: git pull, uninstall the old package (uv pip uninstall cfbd-mcp-server), delete build, dist, and *.egg-info, then reinstall with uv pip install -e . and restart the server.

Q: What happens if I exceed the rate limit? A: The API returns a rate‑limit error; the server recommends adding back‑off logic or caching frequent queries.

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