Chess MCP for Claude — engine analysis, attack maps, game review. One URL, no install.
Returns an engine evaluation of a chess position: the strongest candidate moves with their scores, and the position's checkable facts — side to move, whether that side is in check, undefended pieces and the pieces attacking them, and the material balance. Accepts a FEN, a PGN, or a list of SAN moves from the starting position. Also reports what the OPPONENT would play if it were their turn, which is the difference between a position being quiet and merely looking quiet. Pass considering with a move you are thinking of playing and it returns what that move actually walks into — a losing move is usually not in the top candidates, so asking about it directly is the only way to find out.
| Name | Required | Description | Default |
|---|---|---|---|
| fen | No | FEN of the position. | |
| pgn | No | PGN; the position after the final move is used. | |
| moves | No | SAN moves from the start, e.g. ["e4","e5"]. | |
| traps | No | Also scan the captures and checks available to the side to move and return the ones that lose, each with its refutation. Slower — several extra searches. | |
| multipv | No | How many candidate moves (1-5, default 3). | |
| considering | No | A move you are thinking of playing, in SAN, e.g. "Ng5". Returns the evaluation after it and the line that refutes it. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so safety is covered. The description adds valuable behavioral context: it explains that the tool reports what the opponent would play (distinguishing quiet positions), explicitly warns that the 'traps' parameter is slower due to extra searches, and clarifies that a losing move may not appear in top candidates, requiring the 'considering' parameter. This goes well 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 compact yet rich: the first sentence conveys primary functionality, the second covers input formats, the third introduces a key nuanced behavior, and the fourth explains the 'considering' parameter. Every sentence adds value with no redundancy, and critical details are front-loaded.
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 lacking an output schema, the description fully specifies what the tool returns (candidate moves, scores, checkable facts, opponent's response, refutation lines for 'considering'). It also covers all input formats, optional parameters, and performance trade-offs, leaving no critical gap for an agent to call 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?
Schema description coverage is 100%, so all six parameters already have descriptive text in the schema. The description itself only elaborates on 'considering' and 'traps' behavior, but the schema already covers the rest. Per the baseline rule for high coverage, a score of 3 is appropriate because the description adds minimal extra beyond the schema.
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 verb ('Returns an engine evaluation'), a clear resource (a chess position), and enumerates the concrete outputs (candidate moves, scores, checkable facts). It distinguishes itself from siblings by focusing on engine evaluation and opponent responses, 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?
The description provides clear usage context, such as using the 'considering' parameter to evaluate a specific move when it might not appear in top candidates, and explains that 'traps' adds slower scans. However, it does not explicitly mention when not to use this tool or compare it with sibling tools like chess_review or chess_rig, so it lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns the exact list of squares attacked by the piece standing on a given square in a chess position, together with that piece's type and colour. Returns an empty list when the square is unoccupied. Accepts a FEN, a PGN, or a list of SAN moves from the starting position.
| Name | Required | Description | Default |
|---|---|---|---|
| fen | No | FEN of the position. | |
| pgn | No | PGN; the position after the final move is used. | |
| moves | No | SAN moves from the start, e.g. ["e4","e5"]. | |
| square | Yes | The square, e.g. "d5". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, and the description adds meaningful behavior beyond that: the exact empty-list behavior for unoccupied squares, the output including piece type and colour, and the supported input formats. It does not contradict the annotations, though it could further clarify whether 'attacked' includes squares occupied by friendly pieces or only legal captures in the strict chess sense.
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 tight sentences with no filler. The primary behavior is front-loaded, the unoccupied-square edge case is stated, and the input alternatives are summarized compactly. Every 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?
The description covers return content, an edge case, and input formats, but it does not explicitly state that exactly one of fen/pgn/moves is required alongside square. Since the schema marks all three as optional and only square is required, an agent could attempt an invalid call with just a square. The lack of an output schema also makes a slightly more explicit return-format statement valuable.
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 100%, so the schema already documents all four parameters. The description repeats the FEN/PGN/SAN input options but does not add substantial new meaning beyond what the schema's property descriptions provide, such as the constraint that at least one position source must be supplied.
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 uses a specific verb ('Returns') and names the exact resource: the list of squares attacked by a piece on a given square, plus the piece's type and colour. It is clearly distinct from the broader siblings chess_analyse and chess_review because it targets a precise, narrow computation.
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 makes the tool's domain obvious: call it when you need the squares a particular piece attacks. However, it never explicitly contrasts this with the sibling tools or states when not to use it, leaving the when-to-use decision largely implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Replays a finished game ply by ply with the engine and returns the moves where the evaluation swung by more than a threshold (default 1.5 pawns). Each entry gives the move number, the side that moved, the move played, the engine's preferred move, the size of the swing in pawns, the evaluation before and after, and the FEN of the resulting position. Accepts a PGN or a list of SAN moves, and can be filtered to one colour. Long games are truncated at 60 plies.
| Name | Required | Description | Default |
|---|---|---|---|
| pgn | No | The finished game as PGN. | |
| moves | No | Or SAN moves from the start. | |
| colour | No | "white" or "black" — whose mistakes to report. Default both. | |
| threshold | No | Swing in pawns that counts as a turning point. Default 1.5. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations only declare readOnlyHint=true, and the description adds substantive behavior beyond that: engine replay, threshold default of 1.5 pawns, output fields per entry, supported inputs, colour filtering, and 60-ply truncation. These details make the tool's behavior predictable without contradicting the read-only annotation.
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 with no fluff. It front-loads the core purpose, then packs necessary operational details into the remaining sentences. Every 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?
With no output schema, the description takes on the burden of explaining return values, and it does: each entry's fields are enumerated. It also covers accepted inputs, configuration options, default behavior, and truncation, making the tool self-contained for an agent to use 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?
Schema coverage is 100%, so the baseline is 3, but the description adds real semantic value: it clarifies that pgn and moves are alternatives ('or SAN moves'), explains the colour filter, and states the default threshold value of 1.5 pawns. This goes beyond what the schema alone provides.
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 uses a specific verb and resource: it replays a finished game and returns moves where the engine evaluation swung by a threshold. The 'turning points' concept clearly distinguishes this from the sibling tools chess_analyse and chess_attacks, even without naming them.
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 clear context for when to use the tool: after a game is finished, when you want engine-identified turning points based on evaluation swings. It mentions input options, colour filtering, and the 60-ply truncation, but it does not explicitly name alternatives or say when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Returns the JavaScript source of the in-page coaching rig for a chess.com bot game, to be run in that page by the browser's javascript tool. Once running, the page exposes window.__coachWait(ms) (resolves with events and board status), window.__coachWatch.status() (moves, fen, turn, myTurn, result) and window.__coach (beginDraw, play, dashed, label, step, say) for drawing on the board. Also returns the rig version and the URL of the chess.com bot surface.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as read-only, and the description aligns by stating it 'Returns' source code rather than mutating state. It also goes beyond annotations by detailing the exposed API (window.__coachWait, window.__coachWatch.status, window.__coach) and noting it also returns the rig version and bot surface URL, giving a full picture of what the returned payload does when executed.
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 sentences pack essential information without fluff: the main return value, the runtime behavior, the exposed API details, and auxiliary return values. The most important claim is front-loaded ('Returns the JavaScript source'), and each subsequent detail earns its place by clarifying what the returned code provides.
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 read-only tool with no parameters and no output schema, the description fully equips an agent to invoke it correctly. It states the target context (chess.com bot game), how to use the returned source (run in page via javascript tool), what the source exposes, and what other data is returned (version and URL). Nothing necessary is missing.
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?
There are no parameters, so the baseline is 4. The description does not need to explain parameter semantics because none exist; the zero-parameter schema is fully covered by the absence of parameters.
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 identifies a specific verb ('Returns') and resource ('the JavaScript source of the in-page coaching rig for a chess.com bot game'), making the tool's purpose unmistakable. It also distinguishes itself from the sibling analysis tools by focusing on an in-browser rig rather than analysis or review.
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 context: the returned source is to be run in the chess.com bot game page via the browser's javascript tool. It implies the use case but does not explicitly contrast with alternative tools or state when not to use it. Still, the context is strong enough for an agent to select this tool when a live-coaching rig is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
chess_analyse1 field changedInput schema / properties / trapsAdded value: +{
+ "description": "Also scan the captures and checks available to the side to move and return the ones that lose, each with its refutation. Slower — several extra searches.",
+ "type": "boolean"
+}chess_rigchess_analyse1 field changedInput schema / properties / consideringAdded value: +{
+ "description": "A move you are thinking of playing, in SAN, e.g. \"Ng5\". Returns the evaluation after it and the line that refutes it.",
+ "type": "string"
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Each tool targets a distinct aspect of chess analysis: position evaluation, attack squares, game review, and browser rig generation. No two tools overlap in purpose, so an agent can easily select the correct one.
All tools share the 'chess_' prefix and follow a verb-like second word (analyse, review, rig), except 'attacks' reads as a noun rather than an action. This minor deviation is easily readable and the pattern is otherwise consistent.
With only 4 tools, the server feels compact but not under-scoped. The tools cover essential chess analysis tasks, and each one earns its place, though a few more could round out the surface.
The server covers analysis, tactical awareness, full game review, and even a browser integration rig. Missing common features like move validation or opening lookup, but the core workflows are well-supported for its apparent purpose.