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bb-gnubg-mcp

bb-gnubg-mcp

by hbomze
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

bearoff_id_2_pos

Convert a bearoff ID into a 6-point backgammon position tuple, enabling board state reconstruction for analysis and evaluation.

Instructions

Convert a bearoff ID to a 6-point position tuple.

Args: bearoff_id: Either an integer bearoff ID or a 6-element list.

Returns: Dict with the 6-point position (checkers on points 1-6).

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": {
      "bearoff_id": "<bearoff_id>"
    },
    "name": "bearoff_id_2_pos"
  }
}

Input Schema

NameRequiredDescriptionDefault
bearoff_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description must fully disclose behavior. It mentions the return type (dict with 6-point position) but leaves the list input behavior unexplained. The dual input types (integer vs list) create a significant ambiguity: does a list get converted to an ID, or returned as a position tuple? No error handling or edge cases are described.

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?

The description is concise and well-structured, with a one-sentence summary followed by an args/returns block. It is appropriately sized, though the ambiguity around the list input could be resolved with one additional clarifying clause without bloating the text.

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

Completeness2/5

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

The tool is simple, but the description lacks completeness due to the unclear handling of the 6-element list input. The presence of an output schema reduces the need to detail return values, but the input behavior is not fully specified, and no usage context is provided. This is a notable gap for an agent to invoke the tool correctly.

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 0%, so the description is the only source of parameter meaning. It adds that the list must have 6 elements, but does not explain what those elements represent, how they relate to the integer ID, or what range/format the integer should take. The added constraint is helpful but incomplete.

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?

The description states a clear verb and resource: 'Convert a bearoff ID to a 6-point position tuple.' However, the input schema allows a 6-element list, which the description does not reconcile with the stated purpose, introducing ambiguity about whether the tool also converts positions to bearoff IDs or handles lists differently.

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?

No guidance is provided on when to use this tool compared to siblings like bearoff_probabilities or position_id. There are no usage contexts, exclusions, or alternatives mentioned, leaving the agent without decision support.

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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