nfl-mcp

MCP server for NFL data (2013–2025), powered by nflreadpy and DuckDB. Query play-by-play, rosters, injuries, stats, and more using natural language in Claude Code, VS Code, or Claude Desktop.

Ask Claude questions like:

• "Who had the best EPA per play in 2024?"
• "Show me Patrick Mahomes' completion % over expected by season"
• "Compare 4th quarter red zone efficiency for KC vs PHI in 2023"
• "Which defenses had the highest sack rate in 3rd & long situations?"
• "Who was on IR for the Eagles in Week 10, 2023?"
• "Show me snap count trends for the Chiefs receiving corps in 2024"

Quickstart

pip install nfl-mcp        # or: uvx nfl-mcp
nfl-mcp init               # configure, load data, and start the server

init walks you through setup and offers to start the server immediately when done. No database server to install. No credentials to manage. Data is stored locally in DuckDB.

Deploy to Azure

Run the server in the cloud as an Azure Container App with one click:

The button opens the Azure portal's Custom deployment blade prefilled from infra/azuredeploy.json. Pick a resource group, then Create. It provisions a Container Apps Environment, a Log Analytics workspace, and the Container App (public HTTPS ingress on port 8000). When the deployment finishes, the mcpUrl output is your endpoint — point any MCP client at https://<app>.<region>.azurecontainerapps.io/mcp.

The data is baked into the image. The full DuckDB database is built into the container image at build time, so the app serves read-only with no runtime ingest — it starts instantly, never re-downloads data, needs no external storage, and runs comfortably on 0.5 vCPU / 1Gi. To refresh the data, rebuild the image (re-run the publish workflow); the Publish container image workflow also rebuilds weekly. The replica is pinned to a single instance (minReplicas = maxReplicas = 1).

One-time setup before the button works:

1. The Publish container image workflow must have pushed an image to ghcr.io/ebhattad/nfl-mcp (it runs weekly, on each GitHub release, or manually via Actions → Run workflow). The build ingests all default datasets, so it takes longer than a normal image build.
2. Make that GHCR package public: repo → Packages → nfl-mcp → Package settings → Change visibility → Public. The ARM template pulls the image without credentials, so it must be public.

Prerequisites

• Python 3.10+
• uv (recommended) or pip

Setup

1. Initialize

nfl-mcp init

The wizard will:

1. Configure the local DuckDB database path
2. Download the default NFL datasets (play-by-play, rosters, stats, injuries, and more)
3. Auto-configure your IDE (Claude Desktop and/or VS Code)
4. Offer to start the server immediately

Options:

--skip-ingest       Configure without loading data

2. Start the server

init offers to start the server for you. If you need to start it manually later:

nfl-mcp serve
nfl-mcp serve --port 9000
nfl-mcp serve --host 0.0.0.0

The server uses the MCP Streamable HTTP transport. Point any MCP client at http://<host>:<port>/mcp. The server also sends permissive CORS headers, so browser-based clients (e.g. the MCP Inspector) can connect — their preflight OPTIONS requests are answered instead of rejected.

Note: The server must be running for your IDE to connect. Run nfl-mcp serve in a terminal and keep it open.

3. Verify

nfl-mcp doctor

Checks database connectivity, loaded data, and IDE configuration.

4. Manual client configuration (optional)

If you skipped IDE setup during init, or need to reconfigure:

nfl-mcp setup-client                    # auto-detect clients
nfl-mcp setup-client --client vscode    # VS Code only
nfl-mcp setup-client --client claude-desktop

Or configure manually. Add to .vscode/mcp.json (VS Code):

{
  "servers": {
    "nfl": {
      "url": "http://localhost:8000/mcp"
    }
  }
}

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (Claude Desktop):

{
  "mcpServers": {
    "nfl": {
      "url": "http://localhost:8000/mcp"
    }
  }
}

CLI Reference

nfl-mcp init               Interactive setup wizard
nfl-mcp serve              Start the MCP server (Streamable HTTP, default port 8000)
nfl-mcp ingest             Load NFL data into the database
nfl-mcp update             Refresh the current season as new games are played
nfl-mcp setup-client       Configure IDE MCP clients
nfl-mcp doctor             Health check

Serve options

nfl-mcp serve
nfl-mcp serve --port 9000
nfl-mcp serve --host 0.0.0.0
nfl-mcp serve --auto-update              # refresh the current season while serving

Ingestion options

nfl-mcp ingest                          # default datasets, all available seasons
nfl-mcp ingest --dataset all            # every dataset
nfl-mcp ingest --dataset schedules      # one specific dataset
nfl-mcp ingest --dataset pbp --dataset injuries   # multiple datasets
nfl-mcp ingest --start 2020 --end 2024  # limit to a season range
nfl-mcp ingest --fresh                  # re-ingest even if already loaded
nfl-mcp ingest --list                   # show all available dataset names

Ingest is idempotent — re-running skips datasets and seasons already in the database.

In-season updates

nflverse rewrites the current season's files as games finish. nfl-mcp update refreshes only that season, leaving the rest of the database alone — it checks a published timestamp first and downloads only when that timestamp has moved.

nfl-mcp update                        # one pass over the current season
nfl-mcp update --watch --interval 2h  # keep polling
nfl-mcp serve --auto-update           # or refresh while serving, no downtime

Don't run update against a database a server already holds open — DuckDB allows a single writer. serve --auto-update sidesteps that by building a refreshed copy and swapping it in, and works in Docker via NFL_MCP_AUTO_UPDATE=1 (off by default; mount a named volume at /data to keep the result). The file grows with each refresh, since DuckDB doesn't reclaim deleted pages; nfl-mcp ingest --fresh compacts it.

Datasets

All data is sourced from nflverse via nflreadpy and stored locally in DuckDB. Every dataset below is ingested by default — nfl-mcp ingest loads the full nflverse family so any data a client might need is already there.

Season coverage: 2013 onward. Season-based tables are ingested from 2013 — the window where every nflverse source is complete and consistent — through the current season. Datasets that begin later (e.g. Next Gen Stats 2016, FTN charting 2022) start at their first available season. Non-seasonal reference tables (draft picks, combine, contracts, players) carry their full historical record.

nfl-mcp ingest                 # load the full nflverse family (default)
nfl-mcp ingest --list          # see all dataset names
nfl-mcp ingest --dataset pbp   # load just one dataset

MCP Tools

Key columns in plays

• epa — expected points added (the best single-play quality metric)
• wpa — win probability added
• posteam / defteam — offensive/defensive team abbreviations
• passer_player_name / rusher_player_name / receiver_player_name
• play_type — 'pass' | 'run' | 'field_goal' | 'punt' | 'kickoff' | ...
• desc — raw play description (use ILIKE for text search)

Local Development

git clone https://github.com/ebhattad/nfl-mcp
cd nfl-mcp
pip install -e ".[dev]"

nfl-mcp ingest --dataset all --start 2024 --end 2024
nfl-mcp serve   # server available at http://localhost:8000/mcp
pytest
pytest -m unit     # unit tests
pytest -m integration  # integration tests (requires loaded DB)

Troubleshooting

• nfl-mcp doctor is the fastest way to verify config, database, and client setup.
• If tools return database errors, run nfl-mcp ingest to ensure data is loaded.
• You can override the DB location with NFL_MCP_DB_PATH=/path/to/nflread.duckdb.
• Re-running nfl-mcp ingest is safe — it skips anything already loaded.

License

MIT