bb-gnubg-mcp

by hbomze

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

moves

Generate all legal moves for a backgammon board and dice roll, returning each move's notation, steps, and position key.

Instructions

Generate all legal moves for a board and dice roll.

Args: board_input: Board in any supported format. dice: Dice roll as [die1, die2], each 1-6. player: Who is on roll ("x" or "o"). Default "x".

Returns: Dict with list of legal moves, each with notation, steps, and position key.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "board_input": "<board_input>",
      "dice": "<dice>"
    },
    "name": "moves"
  }
}

Input Schema

Name	Required	Default
`dice`	Yes
`player`	No	x
`board_input`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 9, 2026

TDQS

A4.1/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden of disclosure. It mentions the return format (dict with notation, steps, position key) and accepts multiple board formats, which is useful. However, it does not comment on side effects (e.g., whether the board is mutated), error handling, or edge cases like no legal moves. The description is adequate but not deeply transparent.

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?

The description is efficiently structured with a one-line summary followed by Args and Returns sections. Every sentence provides necessary information without redundancy or filler. It is concise while delivering complete parameter and return documentation.

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?

Given the tool's complexity (backgammon move generation), the description covers the input formats, dice roll specification, player default, and return structure. It lacks only minor behavioral details such as handling of invalid dice or no-legal-move scenarios, but overall it is sufficiently complete for an AI agent to use correctly.

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

Parameters5/5

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

The schema has 0% description coverage, but the tool description compensates fully with an Args section explaining board_input (any supported format), dice (as [die1, die2], each 1-6), and player (x/o, default 'x'). This adds significant meaning beyond the bare schema and leaves no parameter vague.

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 'Generate all legal moves for a board and dice roll' with a specific verb and resource. It distinguishes itself from siblings like 'best_move' (which focuses on selecting a move) and 'roll' (which simulates dice), making the tool's purpose unambiguous.

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

Usage Guidelines3/5

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

The description explains what the tool does but does not explicitly state when to use it over alternatives or provide exclusions. While the context of 'moves' alongside siblings implies it is for move enumeration, there is no direct comparison to 'best_move' or other tools, leaving usage guidance implicit rather than explicit.

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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