Skip to main content
Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
STOCKFISH_PATHNoPath to the Stockfish binary. If not set, the system PATH is searched.autodetect
CHESS_COACH_GAME_DEPTHNoDefault depth for analyze_game.14
CHESS_COACH_DIAGNOSE_DEPTHNoDefault depth for diagnosis (lower = faster).12
CHESS_COACH_ENGINE_HASH_MBNoEngine hash size in MB.128
CHESS_COACH_ENGINE_THREADSNoNumber of engine threads.1
CHESS_COACH_POSITION_DEPTHNoDefault depth for analyze_position.16

Capabilities

Features and capabilities supported by this server

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
engine_statusA

Report whether the local Stockfish engine is available and its version.

Call this first if other tools fail with an engine error.

analyze_positionA

Evaluate a single position (FEN) and return the top engine lines.

Returns evaluation in centipawns and win%, the best lines in SAN, material
balance, and simple tactical flags (fork threats, pins). Use this to explain
*why* a move is best in a specific position.
analyze_gameA

Analyse a full game from PGN: per-move classification + motifs + summary.

Each move gets a classification (best/good/inaccuracy/mistake/blunder with
Korean labels), win% before/after, centipawn loss, the engine's preferred
move, and tactical-motif tags. The summary gives per-side accuracy/ACPL,
per-phase breakdown, and the worst moments (with FENs for drilling).

Set user_color to 'white' or 'black' to focus the worst-moment and motif
reporting on one player.
fetch_recent_gamesA

Fetch a player's recent games from Lichess or Chess.com (public data).

source: 'lichess' or 'chesscom'. speed (optional) filters by
bullet/blitz/rapid/classical. Returns normalised game records including PGN,
players, ratings, result and opening. No analysis is run here.
diagnose_weaknessesA

Diagnose a player's recurring weaknesses across their recent games.

Fetches recent games, analyses each from the player's perspective, and
aggregates: overall accuracy/ACPL, per-phase (opening/middlegame/endgame)
breakdown, recurring tactical blind spots (with example positions), leaky
openings, a time-trouble proxy, and a ranked list of top weaknesses with
example FENs. This is the core 'coach' tool — heavier (runs the engine over
many games), so keep max_games modest for fast turnaround.
recommend_drillsA

Build a personalised drill set from the player's own mistakes.

Runs diagnose_weaknesses, then returns re-solvable drill positions taken
from the player's actual blunders (FEN, side to move, the engine's preferred
move as the answer), ordered to target their top weaknesses, plus an
optional Lichess daily puzzle as a warm-up.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/parkseokjune/chess-coach-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server