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bb-gnubg-mcp

bb-gnubg-mcp

by hbomze
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

board_from_position_id

Convert a 14-character GNUBG Position ID to a board representation.

Instructions

Convert a 14-character GNUBG Position ID to a board representation.

Args: position_id: A 14-character GNUBG Position ID string (e.g. "4HPwATDgc/ABMA").

Returns: Dict with the 2x25 board array and the position ID.

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": {
      "position_id": "<position_id>"
    },
    "name": "board_from_position_id"
  }
}

Input Schema

NameRequiredDescriptionDefault
position_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the responsibility for behavioral disclosure. It transparently describes the return value as a dict with a 2x25 board array and the position ID, giving insight into what to expect. However, it does not mention potential error handling or validation behavior, which is a minor gap for a conversion tool.

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 concise and well-structured: a one-sentence summary followed by Args and Returns sections. Every sentence contributes meaning without waste, and the format is easy to parse for an AI agent.

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 simplicity (one parameter, straightforward conversion), the description is largely complete. It explains the input, output, and even the return structure. The only missing element is contextual guidance about when to use this over other conversion tools, but that is already factored into the usage guidelines dimension.

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 only defines the parameter as a string, providing zero description. The description compensates fully by stating the parameter is a 14-character GNUBG Position ID and providing a concrete example ('4HPwATDgc/ABMA'). This gives the agent all necessary information to supply a valid input.

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: converting a 14-character GNUBG Position ID to a board representation. It specifies the exact resource (GNUBG Position ID) and the output (board representation), which distinguishes it from sibling tools like board_from_position_key that likely handle a different format.

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?

The description provides no guidance on when to use this tool versus alternatives such as board_from_position_key or position_id. It only describes what the tool does, not the circumstances that would make it the appropriate choice, leaving the agent without context for tool selection.

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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