MCP Poker
An MCP server that provides poker play recommendations by combining real-time Monte Carlo equity calculations with historical player tracking and exploit-based advice. It enables users to import PokerNow hand histories to analyze player tendencies and receive data-driven coaching for various game situations.
README
MCP Poker
An MCP server for poker play recommendations — real equity calculations, player tracking, and exploit-based recommendations.
How It Works
- Equity engine: Monte Carlo simulation using
phevaluator(a fast C-based poker hand evaluator). Suits, flushes, and all hand types are fully accounted for. No hardcoded hand rankings — everything is computed. - Player tracking: Imports PokerNow hand histories, stores all hands/actions in SQLite, and computes detailed player statistics (VPIP, PFR, 3-bet, c-bet, aggression, WTSD, etc.)
- Exploit engine: Detects player leaks from historical data and adjusts GTO recommendations to exploit specific tendencies.
- Multi-way aware: Advice adjusts based on number of players in the hand — bluffing less in multi-way, tightening value ranges, accounting for equity dilution.
Architecture
src/ # TypeScript source (MCP server)
├── index.ts # Entry point — wires tools to MCP server
├── tools/
│ ├── import.ts # import_pokernow_log, set_hero, set_player_alias, add_player_note
│ ├── player.ts # get_player_stats, get_player_tendencies, get_player_profile, list_players
│ ├── recommend.ts # recommend_action (main coaching tool)
│ ├── history.ts # search_hands, analyze_hand, get_session_summary, list_sessions
│ └── guide.ts # get_tool_guide (LLM documentation)
├── gto/
│ ├── preflop-ranges.ts # Equity-driven preflop advice (no hardcoded ranges)
│ ├── postflop.ts # MC equity calculator bridge + postflop advice engine
│ └── exploit.ts # Player leak detection + exploit adjustments
├── db/
│ ├── schema.ts # SQLite schema + auto-migration
│ └── queries.ts # All DB queries + stat calculations + player profiles
└── types.ts # Shared TypeScript types
python/ # Python (equity calculations, CSV parsing)
├── equity_calculator.py # Monte Carlo equity calculator using phevaluator
├── parse_pokernow.py # PokerNow CSV log parser
└── requirements.txt
data/ # SQLite database (created at runtime, gitignored)
src/= TypeScript source codedist/= Compiled JavaScript output (gitignored, built bynpm run build)python/= Python scripts called by the TS server.venv/= Python virtual environment with phevaluator
Setup
Prerequisites
- Node.js >= 18
- Python 3.8+
Install
npm install
npm run build
python3 -m venv .venv
.venv/bin/pip install phevaluator
Add to Cursor
The .cursor/mcp.json is already configured. After opening this project in Cursor, the server should be available. If not, add to your MCP config:
{
"mcpServers": {
"poker": {
"command": "node",
"args": ["/path/to/mcp-poker/dist/index.js"]
}
}
}
Tools (14 total)
| Tool | Description |
|---|---|
import_pokernow_log |
Import a PokerNow CSV hand history file |
set_hero |
Set which player is you |
set_player_alias |
Link two PokerNow IDs as the same person |
add_player_note |
Save a note about a player |
get_player_stats |
Full stat sheet (VPIP, PFR, 3-bet, c-bet, AF, WTSD, etc.) |
get_player_tendencies |
Natural language leak analysis + exploit recommendations |
get_player_profile |
Comprehensive behavioral dossier (positional, multi-way, trends, showdowns) |
list_players |
List all tracked players |
recommend_action |
Real-time coaching with Monte Carlo equity + exploit adjustments |
search_hands |
Query hand history with filters |
analyze_hand |
Deep replay of a specific hand |
get_session_summary |
Session results + biggest pots |
list_sessions |
List all imported sessions |
get_tool_guide |
Returns full documentation on all tools for the LLM |
For detailed input/output specs for every tool, call get_tool_guide from the LLM or see src/tools/guide.ts.
Data Storage
All data is stored locally in data/poker.db (SQLite). Nothing leaves your machine.
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