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chessceo

chess ceo

get_player_preparation

Analyze a player's chosen moves and win rates in any opening position, using their actual game history to identify scoring weaknesses and low-variety lines for targeted preparation.

Instructions

For a given player, colour and starting position, return both the moves the player actually chose (frequency + win rate) and the underlying games. Position is specified either as a move sequence in SAN (line) or a raw FEN. Use line iteratively to walk the opening tree: call once with empty line, pick a move, call again with line extended by that move, etc. When preparing for a real game, weight recent games (last 12-24 months) more heavily than old ones, classical over-the-board > rapid/blitz > online, and look for variations the player scores poorly in (below ~40%) or plays with less variety (shallower prep).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fenNoAlternative to `line` — raw FEN of the target position.
lineNoMove sequence in SAN, space-separated, no move numbers required. Example: 'e4 e5 Nf3'. Leave empty for the starting position.
colorYesWhich colour the player is analysed with.
limitNoNumber of games to return (max 10 per request; page with offset).
offsetNo
fide_idYesFIDE ID from search_player.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Describes input specification and iterative behavior well, but does not mention data freshness, rate limits, or edge cases like no games found. Since annotations are absent, more disclosure would help.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single paragraph of moderate length, front-loaded with main purpose. Could benefit from bullet points for readability but remains concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers usage well but does not describe return value structure (format of frequency/win rate, game objects). Without output schema, this is a notable gap for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 83% of parameters; description adds value by explaining how to use `line` iteratively and weighting strategy. Does not repeat schema but provides usage context beyond parameter names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns moves (frequency + win rate) and underlying games for a given player, color, and starting position. It distinguishes from siblings by specifying iterative tree navigation with `line`.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly explains iterative usage of `line` to walk the opening tree, and provides weighting strategy for preparation (recent games, classical over rapid, etc.). No explicit when-not-to-use, but context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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