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analyze_position

Read-only

Rank legal moves in board games by searching from the current position with MCTS, minimax, or ISMCTS to choose your next move.

Instructions

Rank your legal moves with a search from the current position. It only uses information you have.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_kNoHow many of the best moves to return.
game_idYesId returned by new_game.
secondsNoSearch time for Monte Carlo algorithms.
algorithmNoSearch to run. auto uses MCTS for perfect-information games and MO-ISMCTS for Phantom.auto
minimax_depthNoDepth (moves ahead) for minimax.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteYes
movesYesBest moves first.
metricYes
game_idYes
secondsYes
algorithmYes
best_moveYes
iterationsYesSearch iterations, or positions evaluated for minimax.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds a genuinely useful behavioral trait beyond that: 'It only uses information you have', signalling no hidden-information leakage or cheating. It does not mention cost/latency of the searches, but the seconds/top_k schema parameters carry that.

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

Conciseness5/5

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

Two tight sentences with no waste; the primary action is front-loaded and the behavioral qualifier follows. Nothing needs trimming.

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

Completeness4/5

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

An output schema exists so return values need not be described, and all parameters are fully documented in the schema. The description covers purpose and the fairness trait, leaving only sibling routing guidance as a gap, which is a minor completeness issue for this complexity level.

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

Parameters3/5

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

Schema coverage is 100% and each parameter (top_k, seconds, algorithm, minimax_depth, game_id) is documented in the schema, including the algorithm semantics and minimax depth. The description adds no parameter-level detail, so the baseline 3 applies.

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

Purpose4/5

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

States a specific verb (rank) and resource (your legal moves) with the scope 'from the current position', which clearly separates it from play_move and get_state. However, it never names or contrasts a sibling explicitly, so it stops just short of a 5.

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

Usage Guidelines2/5

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

There is no explicit guidance on when to invoke this versus get_state, get_rules, or play_move, and no stated prerequisites beyond the implied 'a game must exist'. The reader must infer that this is a pre-move analysis step.

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