Nfl

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:

Deploy to Azure

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.

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 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

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.

Datasets

All data is sourced from nflverse via nflreadpy and stored locally in DuckDB.

Table	Default	Coverage
plays	✓	1999–present
schedules	✓	1999–present
rosters	✓	1920–present
player_stats	✓	1999–present
team_stats_raw	✓	1999–present
injuries	✓	2009–present
snap_counts	✓	2012–present
teams	✓	current
players	✓	all-time
contracts	✓	historical
trades	✓	historical
depth_charts		2001–present
rosters_weekly		2002–present
ff_opportunity		2006–present
officials		2015–present
nextgen_stats_*		2016–present
participation		2016–2024
pfr_advstats_*		2018–present
ftn_charting		2022–present
draft_picks		1980–present
combine		all-time
ff_playerids		current
ff_rankings_draft		current
ff_rankings_week		current

nfl-mcp ingest --dataset all   # load everything
nfl-mcp ingest --list          # see all dataset names

MCP Tools

Tool	Description
nfl_schema	Database schema reference — compact summary by default, pass category for detail
nfl_status	Database health: total plays, loaded seasons, available tables
nfl_query	Raw SQL SELECT for custom queries (500 row cap, 10s timeout)
nfl_search_plays	Find plays by player, team, season, season type, situation, touchdowns, etc.
nfl_team_stats	Pre-aggregated team offense, defense, and situational stats
nfl_player_stats	Player stats by season and season type — passing, rushing, or receiving
nfl_compare	Side-by-side comparison of two teams or two players
nfl_schedule	Game schedule and results — scores, spread, weather, coaches
nfl_roster	Team roster by season and position
nfl_injuries	Player injury report status by team, week, and designation
nfl_snap_counts	Offensive, defensive, and special teams snap counts per player
nfl_fantasy_opportunity	Target share, air yards share, carry share per player per week (2006–present, requires ff_opportunity dataset)
nfl_catalog	List all loaded tables with row counts and last refresh time

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