K2 Study Decks & Algorithm

get_cube_algorithms

Read-only

Rubik's cube last-layer algorithm sets used by the K2 cube trainer: full OLL (57 cases), full PLL (21), the two-look beginner subsets, full ZBLL, and Roux CMLL (42 cases plus a two-look subset; CMLL algorithms only need to solve the corners and keep both 1x2x3 blocks). Each case returns its primary algorithm plus alternates, with sources and finger-trick notes where recorded. Every algorithm in these sets is verified against a cube simulator by the repo's audit script (app/cube/scripts/audit-algorithms.mjs), run before data changes land. Filter by case_id or group; omit both for the set's structure and levels.

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": {
      "set": "<set>"
    },
    "name": "get_cube_algorithms"
  }
}

Input Schema

Name	Required	Description
`set`	Yes	Algorithm set id
`group`	No	Case group filter, e.g. 'T Shapes', 'OCLL'
`limit`	No	Max cases (default 20, max 50)
`case_id`	No	Single case id, e.g. 'oll-21' or 'pll-t'

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 26, 2026

changedInput schema / properties / set / enum

Previous value: -[
-  "oll",
-  "pll",
-  "two-look-oll",
-  "two-look-pll",
-  "zbll"
-]New value: +[
+  "oll",
+  "pll",
+  "two-look-oll",
+  "two-look-pll",
+  "zbll",
+  "cmll",
+  "two-look-cmll"
+]

First observedOct 23, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Beyond readOnlyHint it discloses the return shape ('primary algorithm plus alternates, with sources and finger-trick notes'), the data provenance (verified by app/cube/scripts/audit-algorithms.mjs), and a domain constraint (CMLL only solves corners, keeps both 1x2x3 blocks). That is substantive behavior an agent cannot infer from annotations.

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?

Front-loaded with the resource and set inventory, then return behavior, then filtering guidance. Dense and useful, though the CMLL parenthetical and the audit-script path add bulk that an agent doesn't strictly need to call the tool.

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

Completeness5/5

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

There is no output schema, so the description carries the full burden of describing returns, and it does: alternates, sources, finger-trick notes, verification status. Combined with complete schema coverage for inputs, an agent has everything needed to call this correctly.

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?

Schema coverage is 100%, so all four parameters are already documented, making 3 the baseline. The description adds real meaning beyond the schema by explaining what omitting the filters yields ('the set's structure and levels') and noting the CMLL case-count context, which helps interpret results.

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?

States a specific verb (returns/gets) and resource (last-layer algorithm sets) and enumerates the exact sets covered (OLL 57, PLL 21, two-look subsets, ZBLL, CMLL 42). An agent can distinguish this bulk-set lookup from sibling explain_k2_algorithm, which explains a single algorithm.

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?

Gives an explicit invocation rule: 'Filter by `case_id` or `group`; omit both for the set's structure and levels.' That tells the agent both retrieval modes. It stops short of naming a sibling alternative (e.g., explain_k2_algorithm for one algorithm's rationale), so it's clear context without 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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