llm-chess-mcp
llm-chess-mcp
Eine MCP-Schach-Laufzeit, die es LLMs ermöglicht, zu spielen, zu analysieren und ihre Stärke anzupassen, ohne jede Entscheidung an eine Engine auszulagern.
Statt einen einzelnen besten Zug zurückzugeben, stellt sie objektive Stärke (Stockfish), menschliche Zugwahrscheinlichkeit (Maia3) und Statistiken aus echten Partien (Lichess) bereit, sodass das LLM wählen kann, wie es spielen möchte. Das LLM übernimmt Strategie und Urteilsvermögen; der MCP-Server erledigt die gesamte Berechnung.
Engines
Engine | Rolle | Laufzeit |
Stockfish 18 (WASM) | Objektive Bewertung, beste Züge, MultiPV | Im Prozess (npm |
Maia3 5M (ONNX) | Menschliche Zugwahrscheinlichkeiten, konditioniert auf Elo | Im Prozess ( |
Lichess-Explorer | Statistiken aus echten menschlichen Partien | HTTP (Token erforderlich) |
Alles läuft innerhalb des Node-Prozesses – es ist weder ein externer Engine-Prozess noch eine Python-Laufzeit zur Bereitstellungszeit erforderlich. Das veröffentlichte Paket enthält das Maia3-5M-Modell; andere Exportvarianten sind keine Laufzeitoptionen, sofern ihre ONNX-Dateien nicht separat bereitgestellt werden.
Related MCP server: Chess MCP
Installation
Erfordert Node.js 20 oder neuer.
Keine Installation erforderlich – direkt mit npx ausführen:
npx -y llm-chess-mcpDas Maia3-Modell ist bereits enthalten, daher müssen weder Python, torch noch Engine-Binärdateien installiert werden. npx lädt das Paket beim ersten Lauf und speichert es im Cache.
Um es stattdessen dauerhaft zu installieren:
npm install -g llm-chess-mcpAus dem Quellcode erstellen
pnpm install
pnpm build
pnpm testpnpm test:unit führt die Unit-Tests aus. pnpm test:e2e erstellt zuerst und führt dann die MCP-Transporttests aus. pnpm check führt die vollständige lokale Prüfung durch; verwenden Sie pnpm release:check vor der Veröffentlichung.
Betreuer
Architektur beschreibt Laufzeit- und Dienstgrenzen.
Lokale Qualitätsbefehle:
pnpm typecheck
pnpm test:coverage
pnpm contract:check
pnpm check
pnpm test:packagepnpm test:stress führt die kurze Echt-Engine-Konkurrenzprüfung durch. pnpm test:live fragt Lichess nur ab, wenn LICHESS_TOKEN gesetzt ist; andernfalls überspringt es ohne Netzwerkanfrage.
Maia3 nach ONNX exportieren (nur zur Build-Zeit)
Dieser Schritt benötigt einmalig Python + PyTorch. Er lädt den Maia3-Checkpoint herunter, verifiziert die Neuimplementierung gegen das Original und exportiert models/maia3-5m.onnx.
uv venv .venv-maia3 --python 3.13
uv pip install --python .venv-maia3/bin/python -r scripts/requirements.txt
uv pip install --python .venv-maia3/bin/python "maia3 @ git+https://github.com/CSSLab/maia3.git@1e13597c42d4858b7cfd7cfdae01e297263364b2"
pnpm export:maia3 # -> models/maia3-5m.onnxDie resultierende .onnx-Datei wird eingecheckt/gebündelt; Endbenutzer benötigen niemals Python oder torch.
Lichess-Token (optional)
Der Eröffnungs-Explorer erfordert jetzt eine Authentifizierung. Generieren Sie ein persönliches Zugriffstoken unter https://lichess.org/account/oauth/token/create und setzen Sie es in .env:
cp .env.example .env
# set LICHESS_TOKEN=...Ohne Token gibt opening_explorer einen Hinweis auf Deaktivierung zurück; alle anderen Tools funktionieren.
Explorer-Filter sind streng. Geschwindigkeiten sind ultraBullet, bullet, blitz, rapid, classical und correspondence; Rating-Stufen sind 0, 1000, 1200, 1400, 1600, 1800, 2000, 2200 und 2500. masters akzeptiert keinen der Filter. Ungültige Filter schlagen lokal fehl. Vorübergehende Fehler (Netzwerk, Timeout, 429 und 5xx) werden einmal innerhalb eines Gesamtbudgets von 12 Sekunden wiederholt; ungültige Anfragen und andere 4xx-Antworten werden nicht wiederholt.
In Ihrem MCP-Client konfigurieren
opencode
Fügen Sie zu opencode.json (Projekt) oder ~/.config/opencode/opencode.json (global) hinzu:
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"llm-chess-mcp": {
"type": "local",
"command": ["npx", "-y", "llm-chess-mcp"],
"enabled": true,
"environment": {
"LICHESS_TOKEN": "your-token"
}
}
}
}Claude Code
Fügen Sie zu .mcp.json (Projekt) oder ~/.claude.json (global) hinzu, oder führen Sie aus:
claude mcp add llm-chess-mcp -- npx -y llm-chess-mcp{
"mcpServers": {
"llm-chess-mcp": {
"command": "npx",
"args": ["-y", "llm-chess-mcp"],
"env": {
"LICHESS_TOKEN": "your-token"
}
}
}
}Codex CLI
Fügen Sie zu ~/.codex/config.toml hinzu:
[mcp_servers.llm-chess-mcp]
command = "npx"
args = ["-y", "llm-chess-mcp"]
[mcp_servers.llm-chess-mcp.env]
LICHESS_TOKEN = "your-token"Oder über die CLI:
codex mcp add llm-chess-mcp --command npx --args -y llm-chess-mcp --env LICHESS_TOKEN=your-tokenTools
Tool | Beschreibung |
| Erstellt eine Partie (optional aus einem FEN), gibt |
| Löscht eine Partie und gibt ihre Sitzung frei |
| Autoritativer Zustand: FEN, Zug, Revision, Schach/Matt/Remis-Flags, Verlauf, letzter Zug, Rochade (optional ASCII) |
| Spielt einen Zug (SAN oder UCI) – das einzige mutierende Tool, mit Schutz vor veralteten Stellungen |
| Alle legalen Züge mit Metadaten |
| Exportiert die Partie als PGN |
| Importiert ein PGN in eine neue Partie |
| Stockfish-MultiPV-Linien (cp/mate/WDL + PV), |
| Maia3-Wahrscheinlichkeiten menschlicher Züge bei einem Ziel-Elo |
| Bewertet einen oder mehrere Züge + cpLoss + Klassifizierung |
| Primäres Tool: vereinheitlichte Kandidaten (objektiv + menschlich + Eröffnung) |
| Komfortschicht: Kandidaten, sortiert nach einer strategischen Absicht |
| Lichess-Statistiken aus echten menschlichen Partien |
Ergebnisformat
structuredContent ist das kanonische erfolgreiche Ergebnis. Fehler auf Handler-Ebene setzen isError und liefern structuredContent.error. Fehler im Eingabeschema werden vom MCP-SDK vor dem Handler generiert und verwenden dessen standardmäßiges isError-Textergebnis ohne structuredContent. Andernfalls ist content nur eine kurze, für Menschen lesbare Zusammenfassung und darf nicht als Daten geparst werden.
Bewertungskonventionen
Stockfish-Werte sind aus der Perspektive der am Zug befindlichen Seite: positives cp = die am Zug befindliche Seite ist besser;
mate N= die am Zug befindliche Seite setzt in N Zügen matt.wdlist[Sieg, Remis, Niederlage]in Promille für die am Zug befindliche Seite.move_candidatesliefertmoverCp(Perspektive des Zugspielers – höher ist besser für den Spieler, der den Zug wählt) undwhiteCp(feste weiße Perspektive), sodass das Vorzeichen nie wechselt.move_evaluatemeldet die Bewertung aus der Perspektive des Zugspielers, pluscpLoss(verlorene Centibauern gegenüber dem besten Zug) und eine Klassifizierung:best / excellent / good / inaccuracy / mistake / blunder.maia3Probist eine Menschlichkeitswahrscheinlichkeit, keine Zugqualität. Ein Zug mit hoher Wahrscheinlichkeit kann objektiv schlecht sein.
Kandidatenstruktur
move_candidates gibt jeden Kandidaten mit drei unabhängigen Facetten zurück:
{
"uci": "g1f3",
"san": "Nf3",
"objective": { "rank": 1, "moverCp": 55, "whiteCp": 55, "cpLoss": 0, "moverMate": null, "wdl": [153, 844, 3] },
"human": { "maia3Prob": 0.62, "selfElo": 1500, "opponentElo": 1500 },
"opening": { "status": "available", "games": 18421, "frequency": 0.31 }
}objective— Stockfish: Engine-Stärke, niemals mit Menschlichkeit verwechselt.moverCpist aus der Perspektive des Zugspielers (höher = besser für den Wählenden).human— Maia3-bedingte Wahrscheinlichkeit bei einem Ziel-Elo.opening— Lichess-empirische Häufigkeit (ein anderes Signal als Maia3).
opening.status ist available, no_data (API ok, aber keine Partien in dieser Stellung), unavailable (Timeout/429/401) oder disabled (kein Token). Stockfish- und Maia3-Ergebnisse werden unabhängig davon immer zurückgegeben.
move_candidates gibt auch moveSensitivity zurück, das beschreibt, wie stark sich die Bewertung über die besten Engine-Linien ändert:
{ "moveSensitivity": { "level": "high", "topMoveSpreadCp": 245 } }level ist low (<80cp-Spanne), medium (80–200cp) oder high (≥200cp). Hohe Sensitivität bedeutet, dass die Wahl unter plausiblen Alternativen die Bewertung wesentlich verändern kann – nützlich, um zu entscheiden, ob man nachlassen oder präzise spielen sollte.
Analyse-Stufen
Stockfish-Tools akzeptieren eine analysis_level-Voreinstellung anstelle roher UCI-Regler:
Stufe | Tiefe | MultiPV |
| 8 | 5 |
| 15 | 8 |
| 22 | 10 |
Explizite depth/multipv-Überschreibungen sind für fortgeschrittene Verwendung weiterhin verfügbar.
Schutz vor veralteten Stellungen
Jeder Zustandslesevorgang gibt eine revision zurück. game_play_move erfordert expected_revision; wenn sich die Partie seit Ihrem letzten Lesen weiterentwickelt hat, wird der Zug abgelehnt:
{ "error": { "code": "STALE_POSITION", "message": "position changed: expected revision 2, current 3" } }Laufzeitgrenzen
Bis zu 1.000 Partie-Sitzungen werden aufbewahrt; inaktive Sitzungen laufen nach einer Stunde ab.
move_evaluateakzeptiert höchstens 10 Züge pro Aufruf.Importierte PGNs sind auf 1 MiB und 4.096 Halbzüge begrenzt.
Stockfish akzeptiert bis zu 32 aktive oder in der Warteschlange befindliche Analysen.
Absichten
move_candidates_by_intent sortiert Kandidaten für eine gewählte Absicht. Es ist eine Komfortschicht über move_candidates; die festen Schwellenwerte unten sind heuristische Standardwerte, nicht die Quelle der Wahrheit:
Absicht | Bedeutung |
| Stärkster Engine-Zug |
| Engine-stark, aber menschlich plausibel |
| Am typischsten für Menschen beim Ziel-Elo |
| Mischung aus Stärke und Menschlichkeit |
| Menschlich plausible Züge, die den Vorteil mäßig reduzieren, ohne das erwartete Ergebnis zu ändern |
| Menschlich plausible Ungenauigkeiten, die die Chancen des Gegners wesentlich verbessern |
Dieses Tool sortiert Kandidaten, wählt aber keinen Zug. Verwenden Sie die zurückgegebenen Signale und den Gesprächskontext, um die endgültige Entscheidung zu treffen – bilden Sie das Können des Benutzers nicht mechanisch auf eine Absicht ab.
Beispielablauf
Die normale Spielschleife besteht aus drei Aufrufen:
create_game→game_idmove_candidates→ einen Zug wählengame_play_move(mitexpected_revision) → ihn ausführen
Gehen Sie nur dann tiefer, wenn Sie es brauchen:
position_analyze— objektive beste Linienhuman_move_distribution— was ein Mensch eines bestimmten Elo spielen würdeopening_explorer— Statistiken aus echten Partienmove_evaluate— einen bestimmten Zug bewerten (oder mehrere vergleichen)
Maia3-ONNX-Verifizierung
Das exportierte ONNX-Modell wird gegen die Upstream-Maia3-Implementierung über feste Stellungen und Elo-Paare regressionstestet:
.venv-maia3/bin/python scripts/verify_maia3.py --model 5mEs prüft die Übereinstimmung der Top-1/Top-k-Züge und den maximalen Wahrscheinlichkeitsfehler, um Export-/Laufzeitregressionen zu erkennen. Das gebündelte maia3-5m.onnx besteht mit 100% Top-1- und Top-5-Übereinstimmung und einem maximalen Wahrscheinlichkeitsfehler < 1e-4.
Paketverifizierung
Paketartefakte werden lokal verifiziert; dieses Projekt hat bewusst keinen gehosteten CI-Workflow.
Führen Sie pnpm check für die deterministische Offline-Prüfung aus. Verwenden Sie pnpm test:package, um das Projekt zu packen, das Tarball in einem sauberen temporären Verzeichnis zu installieren und die installierte llm-chess-mcp-Binärdatei gegen die echten Stockfish- und Maia-Laufzeiten auszuführen. pnpm release:check führt beide Prüfungen plus das Produktionsabhängigkeits-Audit und den Paketmanifest-Trockenlauf aus.
Lizenz & Namensnennung
Dieses Projekt ist unter der AGPL-3.0 lizenziert (siehe LICENSE).
Es bündelt und hängt von Komponenten Dritter ab:
Komponente | Lizenz | Quelle |
Maia3 (Chessformer) | AGPL-3.0 | UofT CSSLab — Monroe et al., Chessformer: A Unified Architecture for Chess Modeling (ICLR 2026) |
Stockfish (via npm | GPL-3.0 | Die Entwickler von Stockfish |
MIT | Microsoft | |
BSD-2-Clause | Jeff Hlywa |
Das gebündelte Maia3-Modell (models/maia3-5m.onnx) ist abgeleitet von
UofTCSSLab/Maia3-5M bei b6559de2398d7140b985f28fd2c19fb5e47ddabe.
Der ONNX-Export ist ein Schritt zur Build-Zeit (scripts/export_maia3.py); die Laufzeitumgebung
führt keinen Maia3-Python-Code aus.
Available Tools
13 toolscreate_gameCreate Chess GameA
Create a new chess game and return its game_id. The server is the authoritative source of board state — never track the board yourself. Optionally pass a FEN to start from a custom position.
| Name | Required | Description | Default |
|---|---|---|---|
| fen | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| game_id | Yes | |
| revision | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint=false) already indicate a write operation, but the description adds the critical behavioral directive to treat the server as the source of truth and avoid local board tracking. It also discloses the return of game_id. This goes beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences and front-loaded with the primary action. Each sentence adds value: the core function, the state-tracking warning, and the optional parameter explanation. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple create tool with one optional parameter and an existing output schema, the description covers the essential aspects: what it does, return value, usage caution, and parameter semantics. It does not mention error behavior or prerequisites, but these are not critical for this tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% coverage for the 'fen' parameter, but the description clearly explains that it is optional and used to start from a custom FEN position. This fully compensates for the missing schema description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a specific verb+resource pair ('Create a new chess game') and explicitly states the return value (game_id), making the tool's purpose unambiguous. It also clearly distinguishes from siblings like delete_game and game_import_pgn.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear usage context: the server is authoritative and one should never track the board locally, which implies how to use this with other game-state tools. It also notes the optional FEN for custom positions but does not explicitly exclude alternatives like game_import_pgn.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_gameDelete Chess GameCDestructiveIdempotent
Delete a game and free its session.
| Name | Required | Description | Default |
|---|---|---|---|
| game_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| deleted | Yes | |
| game_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=false, destructiveHint=true, and idempotentHint=true, so the safety profile is covered structurally. The description adds "free its session" as a mild behavioral note but doesn't disclose permanence of deletion, whether related data (moves, analyses) is destroyed, or access requirements. No contradiction with annotations exists; the description adds modest value over what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exactly one sentence (7 words), front-loaded with the action verb. It's efficient but arguably over-terse — the "free its session" concept and the game_id requirement both deserve elaboration. This borders on under-specification rather than genuine conciseness, though it avoids padding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 1-parameter destructive tool with good annotations and an output schema, the description is minimally adequate. The main gap is the unexplained "free its session" semantic — whether deletion is permanent, whether sessions are tied to games, and what the caller should expect afterward. Low complexity lowers the bar, but the session concept creates real ambiguity the description should resolve.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description fails to compensate. While game_id is a self-descriptive parameter name, the description doesn't explain how to obtain a valid game_id (e.g., from create_game or game_state), any format expectations, or validation constraints. With zero coverage, the description carries the burden and drops the ball.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
"Delete a game" clearly identifies the verb and resource, and it implicitly distinguishes from create_game among siblings. However, "free its session" is vague and unexplained — it's unclear what a session is, what freeing it means, or whether it implies releasing resources beyond the game itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, no mention of prerequisites (ownership, active games, permissions), and no reference to sibling tools like create_game as the inverse operation. It neither states exclusions nor implies usage context beyond the literal action.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
game_import_pgnImport Chess PGNA
Import a PGN into a new game. Returns a new game_id with the position after all PGN moves. Rejects malformed or illegal PGN.
| Name | Required | Description | Default |
|---|---|---|---|
| pgn | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| fen | Yes | |
| turn | Yes | |
| isDraw | Yes | |
| game_id | Yes | |
| history | Yes | |
| isCheck | Yes | |
| castling | Yes | |
| lastMove | Yes | |
| revision | Yes | |
| isGameOver | Yes | |
| moveNumber | Yes | |
| isCheckmate | Yes | |
| isStalemate | Yes | |
| isDrawByFiftyMoves | Yes | |
| isThreefoldRepetition | Yes | |
| isInsufficientMaterial | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With all annotations false, the description carries the transparency burden. It discloses that a new game is created (side effect), returns a game_id, and rejects invalid PGN. However, it does not mention idempotency, potential side effects on other resources, or permission requirements, but the core side effect is well-stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences: action, result, and error handling. No unnecessary details or redundant phrasing. It is well-structured and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, but the description clearly states the output: 'Returns a new game_id.' It also mentions error rejection. It does not describe the error format or potential additional outputs (e.g., full game state), but for a simple import tool, these details are not critical. The description provides sufficient context for a basic understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'pgn' has no description in the schema. The description implies it is a PGN string by the tool name and purpose, and mentions rejection of malformed PGN, but does not elaborate on acceptable format (e.g., whether headers are required) or provide examples. This is minimal but adequate for a standard chess PGN.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Import a PGN into a new game' and its outcome: 'Returns a new game_id with the position after all PGN moves.' This distinguishes it from sibling tools like create_game, game_pgn, and game_play_move by specifying the input format (PGN) and the new game creation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions error behavior ('Rejects malformed or illegal PGN') but does not explicitly indicate when to use this tool versus alternatives, such as when a PGN is available versus starting a blank game with create_game. There is no mention of alternatives or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
game_legal_movesList Legal Chess MovesARead-onlyIdempotent
List all legal moves in the current position (SAN, UCI, piece, capture, promotion).
| Name | Required | Description | Default |
|---|---|---|---|
| game_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| count | Yes | |
| moves | Yes | |
| game_id | Yes | |
| revision | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description doesn't need to restate these. The description adds behavioral context by listing the output fields (SAN, UCI, piece, capture, promotion), which informs the agent about the nature of the response. No contradictions with annotations are present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that clearly states the action and output fields. It contains no filler or redundant information, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no nested objects) and the presence of an output schema, the description adequately covers the necessary context. It specifies the output fields and the operation's scope ('current position'). It could have mentioned edge cases (e.g., no legal moves) or the meaning of 'current position' in terms of move history, but these are not critical for a basic move-listing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter, game_id, with no description coverage (0%). The tool description does not mention game_id at all, leaving its semantics implicit. While the parameter is self-explanatory (identifying the game), the description fails to compensate for the lack of schema descriptions, providing no added meaning beyond the name and type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all legal moves in the current position, specifying the output fields (SAN, UCI, piece, capture, promotion). This distinguishes it from sibling tools like game_play_move (which executes moves) and move_candidates (which may provide candidate analysis), making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage is for retrieving legal moves for the current game identified by game_id, but it does not explicitly contrast with alternatives like move_candidates or position_analyze. The context is clear, but no exclusions or specific when-to-use guidance are given, so a 4 is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
game_pgnExport Chess PGNBRead-onlyIdempotent
Export the current game as PGN.
| Name | Required | Description | Default |
|---|---|---|---|
| game_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| pgn | Yes | |
| game_id | Yes | |
| revision | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description adds no new behavioral context. The word 'Export' is consistent with read-only, but there is no elaboration on side effects, permissions, or response details beyond the output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with zero filler. It efficiently states the tool's function without unnecessary detail, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter and an output schema, so the description covers the core action. However, it lacks any guidance on alternatives or parameter specifics, and the lack of usage context means the description is only minimally adequate for the tool's overall adoption.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must clarify the parameter, but it only refers to 'the current game' without explaining that game_id identifies which game. The lone parameter's meaning is left implicit, forcing the agent to infer from the parameter name alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Export') on a specific resource ('the current game') with a clear output format (PGN). It distinguishes from siblings like game_import_pgn (import) and game_state (state retrieval), making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as game_state or game_import_pgn. The description is a single declarative sentence with no context about scenarios, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
game_play_movePlay Chess MoveADestructive
Play a move (SAN like 'e4' or UCI like 'e2e4') and return the resulting state. This is the ONLY tool that mutates the game. expected_revision is required: pass the revision from your most recent game_state/move_candidates read. If the game has advanced since then, the move is rejected with STALE_POSITION.
| Name | Required | Description | Default |
|---|---|---|---|
| move | Yes | ||
| game_id | Yes | ||
| expected_revision | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| fen | Yes | |
| move | Yes | |
| turn | Yes | |
| isDraw | Yes | |
| game_id | Yes | |
| history | Yes | |
| isCheck | Yes | |
| castling | Yes | |
| lastMove | Yes | |
| revision | Yes | |
| isGameOver | Yes | |
| moveNumber | Yes | |
| isCheckmate | Yes | |
| isStalemate | Yes | |
| isDrawByFiftyMoves | Yes | |
| isThreefoldRepetition | Yes | |
| isInsufficientMaterial | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark destructiveHint=true, but the description adds critical concurrency behavior: the STALE_POSITION rejection if the revision is outdated. It also states it returns the resulting state, which is useful even with an output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences deliver the core action, uniqueness, and concurrency requirement with zero fluff. Each sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutating tool with an output schema and annotations, the description covers the essential operational details: mutation, concurrency control, error condition, and move format. It is complete enough for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero schema description coverage, the description compensates by explaining the 'move' parameter (SAN vs UCI) and the 'expected_revision' parameter (required, from a recent read, and its role in staleness). The 'game_id' is self-explanatory, so overall it adds substantial meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description explicitly states 'Play a move' and even clarifies SAN/UCI formats. The unique claim 'This is the ONLY tool that mutates the game' clearly differentiates it from all siblings, which are read-only or other operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It clearly states when to use this tool (to make a move) and specifies the required expected_revision from a prior read. It does not explicitly mention alternatives, but by highlighting it's the only mutating tool, it implicitly advises that all other tools are non-mutating.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
game_stateGet Chess Game StateARead-onlyIdempotent
Return the authoritative state of a game: FEN, turn, revision, check/mate/draw flags, move history, last move, castling rights. Use this instead of remembering the board. Set include_ascii=true to also get a board diagram.
| Name | Required | Description | Default |
|---|---|---|---|
| game_id | Yes | ||
| include_ascii | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| fen | Yes | |
| turn | Yes | |
| board | No | |
| isDraw | Yes | |
| game_id | Yes | |
| history | Yes | |
| isCheck | Yes | |
| castling | Yes | |
| lastMove | Yes | |
| revision | Yes | |
| isGameOver | Yes | |
| moveNumber | Yes | |
| isCheckmate | Yes | |
| isStalemate | Yes | |
| isDrawByFiftyMoves | Yes | |
| isThreefoldRepetition | Yes | |
| isInsufficientMaterial | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, covering safety and repeatability. The description adds value by specifying the content of the response (fields) and the optional include_ascii behavior, but does not disclose additional behavioral nuances like response format or error conditions beyond what annotations already imply. With strong annotations, this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and fields, followed by an optional usage hint. Every sentence carries value; there is no fluff or redundancy. It is concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (which explains return structure) and the simple parameter set, the description fully covers what the tool does and when to use it. It even lists the key fields for quick understanding and provides a usage example for the optional parameter. It is complete for this tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It explains include_ascii (setting it true provides a board diagram) but leaves game_id implicit, though the name suggests it identifies the game. It adds some meaning for include_ascii, but could have explicitly stated game_id's purpose. Since game_id is evident from context, this partially compensates.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the authoritative game state and enumerates specific fields (FEN, turn, revision, flags, move history, etc.). It differentiates itself from siblings like game_pgn (which likely returns a different format) by focusing on the internal state, and explicitly suggests using it instead of remembering the board.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance to use this tool instead of relying on memory, giving a clear when-to-use context. It does not explicitly mention when not to use it or mention alternatives, but the ton of 'authoritative state' and scope differentiate it from related tools. No exclusions are stated, but the implied usage is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
human_move_distributionEstimate Human Chess MovesARead-onlyIdempotent
Return the Maia3 human-like move probability distribution for the current position, conditioned on a target Elo. Higher probability = more human-typical at that rating. This is NOT move quality — a high-probability move can still be objectively bad.
| Name | Required | Description | Default |
|---|---|---|---|
| elo | No | ||
| top_n | No | ||
| game_id | Yes | ||
| oppo_elo | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| elo | Yes | |
| moves | Yes | |
| game_id | Yes | |
| oppo_elo | Yes | |
| revision | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, covering safety and determinism. The description adds crucial behavioral nuances: it clarifies that higher probability means more human-typical, and explicitly notes that this is not move quality, preventing misinterpretation. It does not contradict annotations, and adds context beyond what annotations provide, such as the meaning of the output values.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—two sentences, no fluff. Every word earns its place: the first sentence defines the function and key input (Elo), and the second provides a critical caveat. It is front-loaded with the core purpose and avoids unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple (read-only, 1 required param) and has an output schema, so the description needn't detail the return structure. It covers the core usage and behavior (human-likeness, Elo conditioning, not-quality warning). The main gap is parameter semantics (covered separately), but overall the description provides sufficient context for an agent to understand what the tool does and when to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must explain parameters. It only mentions 'target Elo' (which maps to elo) but does not clarify top_n (how many moves returned), oppo_elo (opponent rating effect), or game_id (required, identifies position). The word 'distribution' might imply all moves, but top_n suggests a subset, creating ambiguity. This is inadequate for a 4-param tool with no schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the Maia3 human-like move probability distribution for the current position, conditioned on target Elo. It explicitly distinguishes itself from move quality ('This is NOT move quality'), which differentiates it from siblings like move_evaluate or position_analyze. The verb 'Return' and specific resource 'Maia3...distribution' make the purpose precise.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use: when you need human-like probabilities for a given rating, and warns against confusing with move quality. It does not explicitly mention alternatives or when NOT to use it, but the 'NOT move quality' caveat curbs misuse. The 'conditioned on a target Elo' hints at the parameter, but no direct guidance on top_n or oppo_elo is given. So it provides clear context with a partial exclusion but lacks explicit alternative naming.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
move_candidatesGenerate Chess Move CandidatesARead-onlyIdempotent
The primary move-selection tool. Combine Stockfish objective evaluation (moverCp, whiteCp, cpLoss, mate, WDL), Maia3 human probability, and Lichess real-game statistics into a unified candidate list. moverCp is from the mover's perspective: higher = better for the player choosing the move. Use this before choosing a move; the final choice is yours.
| Name | Required | Description | Default |
|---|---|---|---|
| elo | No | ||
| game_id | Yes | ||
| sf_depth | No | ||
| lichess_db | No | lichess | |
| maia_top_n | No | ||
| sf_multipv | No | ||
| analysis_level | No | normal | |
| lichess_speeds | No | ||
| lichess_ratings | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| elo | Yes | |
| fen | Yes | |
| turn | Yes | |
| game_id | Yes | |
| revision | Yes | |
| candidates | Yes | |
| analysis_level | Yes | |
| moveSensitivity | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds valuable behavioral context by explaining that moverCp is from the mover's perspective and that the tool merges three distinct evaluation sources, which goes beyond what annotations or schema convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the tool's primary role, and every sentence adds meaningful context: what the tool does, the moverCp perspective, and when to use it. There is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity and the presence of an output schema and helpful annotations, the description covers the core selection context well. It could be more complete by explicitly contrasting with sibling tools like move_candidates_by_intent, but the 'primary' framing mitigates this gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain any of the nine input parameters such as elo, sf_depth, lichess_ratings, or analysis_level. It adds meaning to the output metrics but fails to compensate for the lack of parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states this is the primary move-selection tool and that it combines Stockfish evaluation, Maia3 human probability, and Lichess statistics into a unified candidate list. This specific verb+resource combination distinguishes it from sibling tools like move_evaluate or move_candidates_by_intent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly instructs the agent to use this tool before choosing a move, which is strong usage guidance. However, it does not name alternatives or provide when-not-to-use conditions, so it falls short of full differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
move_candidates_by_intentRank Chess Moves by IntentARead-onlyIdempotent
Convenience layer over move_candidates: rank candidates for a strategic intent. This tool RANKS candidates but does NOT choose a move — use the returned signals and conversation context to make the final decision. Do not map user skill mechanically to an intent. intents: best (strongest engine move), strong (engine-strong but human-plausible), natural (most human-typical), balanced (blend of strength and human-likeness), ease_off (human-plausible moves that modestly reduce advantage without changing the expected result), give_chance (human-plausible inaccuracies that meaningfully improve the opponent's chances).
| Name | Required | Description | Default |
|---|---|---|---|
| elo | No | ||
| intent | Yes | ||
| game_id | Yes | ||
| sf_depth | No | ||
| lichess_db | No | lichess | |
| maia_top_n | No | ||
| sf_multipv | No | ||
| analysis_level | No | normal | |
| lichess_speeds | No | ||
| lichess_ratings | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| elo | Yes | |
| fen | Yes | |
| turn | Yes | |
| intent | Yes | |
| game_id | Yes | |
| revision | Yes | |
| candidates | Yes | |
| analysis_level | Yes | |
| moveSensitivity | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is covered. The description adds behavioral context by stating it RANKS but does NOT choose a move, and clarifies the meaning of each intent, which is valuable beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph but packs essential information: purpose, relationship to sibling, behavioral caveat, and intent definitions. It is front-loaded with the core purpose and then details intents. Slightly long but each sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 10 parameters, 2 required, and an output schema. The description explains the core intent parameter and the tool's role, but does not cover other parameters like elo, sf_depth, or lichess_db. However, the output schema exists, so return values are covered. The description is adequate for a complex tool but could mention how other parameters affect ranking.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains the 'intent' parameter in detail with definitions for each enum value, which is critical. However, other parameters like elo, sf_depth, lichess_db, etc., are not explained in the description, relying on the schema's names and defaults. Given the complexity, the description covers the most important parameter but leaves others to inference.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a convenience layer over move_candidates that ranks candidates for a strategic intent. It explicitly distinguishes itself from move_candidates and clarifies it does not choose a move, which differentiates it from siblings like game_play_move.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: as a ranking layer over move_candidates, and explicitly warns not to map user skill mechanically to an intent. It also lists all intents with definitions, giving clear context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
move_evaluateEvaluate Chess MovesARead-onlyIdempotent
Evaluate one or more moves with Stockfish without mutating the game. Pass a single move string or an array of moves to compare. Returns, for each move, the score after the move (from the mover's perspective), cpLoss vs the best move, and a classification (best/excellent/good/inaccuracy/mistake/blunder).
| Name | Required | Description | Default |
|---|---|---|---|
| move | Yes | ||
| depth | No | ||
| game_id | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| game_id | Yes | |
| results | Yes | |
| revision | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include readOnlyHint=true and idempotentHint=true, and the description explicitly states 'without mutating the game,' reinforcing that. It also adds value by describing the output (score, cpLoss, classification) and the engine (Stockfish), which are not required but provide useful behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise—three sentences—and front-loaded with the core purpose and non-mutation guarantee. Every sentence adds meaning without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema is present, so detailed return field explanations are unnecessary. The description covers the primary purpose, input flexibility, and key output categories, making it adequate for most usage scenarios. Minor omissions (e.g., depth semantics) are acceptable given the schema's constraints.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description partially compensates by explaining the 'move' parameter format (string or array). However, it does not clarify the 'depth' or 'game_id' parameters beyond what the schema already provides, leaving incomplete semantic coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool evaluates moves using Stockfish, specifying the action (evaluate), resource (moves), and context (without mutating the game). It distinguishes itself from sibling tools like game_play_move (which mutates) and game_legal_moves (which lists legal moves).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies its use for analysis rather than gameplay via 'without mutating the game' and mentions comparing moves, which gives context. However, it does not explicitly name alternative tools or provide exclusion criteria, so it falls short of a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
opening_explorerQuery Lichess Opening ExplorerBRead-onlyIdempotent
Query the Lichess opening explorer for real human game statistics in the current position (requires LICHESS_TOKEN).
| Name | Required | Description | Default |
|---|---|---|---|
| db | No | lichess | |
| speeds | No | ||
| game_id | Yes | ||
| ratings | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| db | Yes | |
| black | Yes | |
| draws | Yes | |
| moves | Yes | |
| white | Yes | |
| game_id | Yes | |
| opening | Yes | |
| revision | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, openWorldHint, idempotentHint) already indicate a safe read-only operation. Description adds environment requirement: the question requires a token. Does not mention permissions, read-only side effects, or returns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Short description of one sentence with no unnecessary details. However, the output or result not mentioned.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite output schema(true), annotations (readOnly/openWorld/idempotent), the description doesn't mention what the tool returns (opening list/names/counts), so the agent may not know if it can fulfill the request. the description is too sparse for interactive decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Description gives no parameter semantics. The input schema covers params and types but does not explain the source or valid values. There are 4 parameters and he documentation does not cover the specific role of `db`, `speeds`, `ratings`, or `game_id`. Coverage is 0% and the description doesn't compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses a specific verb ('Query') with the Lichess opening explorer and the current position context. However, it doesn't distinguish itself from sibling tools other than by name and implicit use case.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives like position_move or game_analysis. The only guidance meaning implied: 'current position' and 'requires a token'. Not enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
position_analyzeAnalyze Chess PositionARead-onlyIdempotent
Run Stockfish on the current position and return the top engine lines (multipv). Scores are from the side-to-move perspective: positive cp = side to move is better; mate N = side to move mates in N. wdl is [win, draw, loss] in permille for the side to move. Use analysis_level (fast/normal/deep) or explicit depth/multipv. Does NOT mutate the game.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | ||
| game_id | Yes | ||
| multipv | No | ||
| analysis_level | No | normal |
Output Schema
| Name | Required | Description |
|---|---|---|
| fen | Yes | |
| turn | Yes | |
| lines | Yes | |
| game_id | Yes | |
| revision | Yes | |
| analysis_level | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, so the description's statement 'Does NOT mutate the game' adds no new information, but it does clarify the evaluation perspective and wdl format, and explains analysis levels without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
All sentences are purposeful and detailed without redundancy, front-loading the core action and then explaining parameters and output semantics efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 4-parameter tool with output schema and strong annotations, the description covers the tool's functionality, parameter choices, and output interpretation (cp, mate, wdl) completely, making it highly usable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description explains the meaning and effect of analysis_level, depth, and multipv, and clarifies that game_id is required to identify the position. This compensates fully for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs Stockfish on the current position and returns top engine lines, distinguishing it from sibling tools like move_evaluate and move_candidates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explains how to specify analysis level or depth/multipv, and mentions it does not mutate the game, but lacks explicit when-not-to-use or alternative tool comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
13 tool updates
v0.3.1- First observed
create_game - First observed
delete_game - First observed
game_import_pgn - First observed
game_legal_moves - First observed
game_pgn - First observed
game_play_move - First observed
game_state - First observed
human_move_distribution - First observed
move_candidates - First observed
move_candidates_by_intent - First observed
move_evaluate - First observed
opening_explorer - First observed
position_analyze
TDQS
Scored across 13 tools
Game lifecycle tools are clearly distinct: create, delete, state, move, legal moves, PGN export/import. The move-analysis tools overlap somewhat—position_analyze, move_evaluate, move_candidates, and move_candidates_by_intent all return evaluation-like signals—but their descriptions clarify different purposes: raw engine lines, per-move comparison, unified candidate selection, and intent-based ranking. An agent could hesitate between these, but they are not truly interchangeable.
Some names follow verb_noun (create_game, delete_game), while others are noun phrases or prefixed differently (game_state, game_pgn, game_import_pgn, position_analyze, opening_explorer). The move_* group is predictable, but the overall naming is a mix of conventions. Names are still readable and snake_case consistent, so the inconsistency is moderate rather than chaotic.
Thirteen tools is well within the ideal range and appropriate for a chess server that handles game lifecycle, PGN import/export, legal move queries, engine analysis, human-move modeling, and opening statistics. Each tool has a defined role, and the count does not feel bloated or thin.
The surface covers the full lifecycle: create, read state, mutate via play move, legal moves, delete, and PGN import/export. Analysis coverage is also strong: Stockfish lines, move evaluation, human move distributions, unified candidates, intent-based ranking, and opening explorer. There are no obvious dead ends for playing or analyzing a chess game.
Maintenance
Related MCP Connectors
Chess MCP for Claude: engine analysis, attack maps, game review. One URL, no install.
Lichess MCP — public read-only API for users, games, explorer, tablebase.
Pedagogical chess intelligence for AI agents: explain positions and games for a target Elo.
MCP server exposing the Backtest360 engine API as tools for AI agents.
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