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College Football Data MCP Server: Real-Time Insights & Play Mirroring - MCP Implementation

College Football Data MCP Server: Real-Time Insights & Play Mirroring

College Football Data MCP Server mirrors every play, stat, and strategy—unleashing real-time insights to outsmart the competition.

Research And Data
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About College Football Data MCP Server

What is College Football Data MCP Server: Real-Time Insights & Play Mirroring?

College Football Data MCP Server is a powerful analytical tool designed to provide real-time access to granular football statistics and predictive analytics. This server integrates with the College Football Data API to enable advanced game analysis, including play-by-play breakdowns, win probability modeling, and comparative performance metrics. It empowers users to mirror live gameplay patterns, track seasonal trends, and generate actionable insights for tactical decision-making.

How to use College Football Data MCP Server: Real-Time Insights & Play Mirroring?

  1. Configuration: Install via package manager and configure environment variables with your API credentials.
  2. Data Querying: Utilize predefined endpoints (e.g., /games, /drives) to fetch structured data using HTTP requests.
  3. Analysis Execution: Deploy built-in templates like analyze-game or compare-teams to process multi-dimensional datasets.
  4. Visualization: Parse JSON responses into dashboards for trend identification and performance benchmarking.

College Football Data MCP Server Features

Key Features of College Football Data MCP Server: Real-Time Insights & Play Mirroring?

  • Dynamic API Integration: Real-time access to over 8+ data schemas including metrics, rankings, and in-game win probabilities.
  • Custom Query Engine: Advanced filtering capabilities for date ranges, team hierarchies, and statistical thresholds.
  • Advanced Analytics: Built-in modules for calculating expected points, turnover impact, and season trajectory analysis.
  • Scalable Architecture: Optimized for handling high-frequency requests while adhering to API rate limits.

Use cases of College Football Data MCP Server: Real-Time Insights & Play Mirroring?

Typical applications include:

  • Tactical Coaching: Identifying opponent weaknesses through historical drive efficiency analysis.
  • Media Production: Generating real-time stats overlays for live broadcasts.
  • Investment Analysis: Evaluating player performance using normalized statistical benchmarks.
  • Academic Research: Correlating game outcomes with weather patterns or venue characteristics.

College Football Data MCP Server FAQ

FAQ: College Football Data MCP Server

Q: How does the rate limiting work?
A: Implements tiered throttling (100 requests/minute for standard users) with exponential backoff on API congestion.
Q: Can I customize analysis templates?
A: Yes, through parameter injection in query strings (e.g., ?metric=expected_points).
Q: What authentication methods are supported?
A: OAuth2 token-based authentication with optional API key fallback for legacy systems.
Q: How is data versioned?
A: Uses semantic versioning in API endpoints (e.g., /v2/games) to ensure backward compatibility.

Content

College Football Data MCP Server

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

Python Version CFBD API Version MIT License smithery badge

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

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
  1. Create and activate a virtual environment:
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
  1. Install dependencies:
uv pip install -e .
  1. 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
  1. Create and activate a virtual environment:
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
  1. Install dependencies:
uv pip install -e .
  1. 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`
  1. 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"
            }
        }
    }
}
  1. Close then restart Claude Desktop

Once you restart you should see a small hammer icon in the lower right corner of the textbox. If you hover over the icon you'll see the number of MCP tools available.

Screenshot of successful install

Updating after install

  1. Download the updated files
cd cfbd-mcp-server
git pull
  1. Uninstall the existing package:
uv pip uninstall cfbd-mcp-server
  1. 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
  1. Install the revised package and its dependencies
uv pip install -e .
uv sync --dev --all-extras

5.

uv run cfbd-mcp-server
  1. 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]"
  1. 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
  1. Rate Limiting
* Space out requests when possible
* Consider Patreon subscription for higher limits
* Implement caching for frequently accessed data
  1. Connection Issues
* Verify internet connectivity
* Check API status at collegefootballdata.com
* Ensure proper error handling in your code

Getting Help

License

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

Acknowledgments

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