bb-gnubg-mcp

by hbomze

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

pub_best_move

Compute an optimal backgammon move for a given board and dice roll using GNU Backgammon's public evaluation engine.

Instructions

Find the best move using GNUBG's public (non-neural, fast) evaluation.

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 move as from/to pairs and notation.

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": "pub_best_move"
  }
}

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

A4.2/5.0

Behavior3/5

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

There are no annotations, so the description must disclose behavior. It states the evaluation type and return format ('Dict with move as from/to pairs and notation'), but does not discuss error handling, non-determinism, or limitations such as lower accuracy relative to neural evaluation. It adds minimal behavioral context beyond the tool name and schema.

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 brief and front-loaded: the first sentence states the purpose, followed by a clean Args/Returns structure. Every line carries necessary information without redundancy.

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?

For a simple computation tool with an output schema, the description covers purpose, parameters, and return type. It lacks explicit usage differentiation and edge-case handling, but overall provides a solid foundation.

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?

With 0% schema coverage, the description compensates by documenting all three parameters: board_input's flexible format, dice's exact structure with range 1-6, and player's default value 'x'. It adds meaning to the bare type definitions, though 'any supported format' remains 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 the tool's function with a specific verb ('Find the best move') and identifies the evaluation method ('GNUBG's public (non-neural, fast) evaluation'), distinguishing it from the sibling tools like 'best_move' which likely uses neural evaluation.

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

Usage Guidelines4/5

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

The description gives context that this is the 'public (non-neural, fast)' evaluation, implying it is the speed-oriented alternative. However, it does not explicitly name alternatives or provide when-to-use/when-not-to-use guidance, so it provides clear context but no exclusions.

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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