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List Jev tuning knobs

get_tuning_cards
Read-only

List the Title's tuning knobs: ready-made variants of its look (colour themes, backgrounds, motion, sound sets), feel, rewards, quests, shop and texts that fit this Title, what is set now, what the decision model suggested last time and which knobs the publisher pinned (the decision model never changes a pinned knob). Free. Use the knob and option ids from here as picks in tuning_preview / tuning_apply.

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": {
      "title_id": "<title_id>"
    },
    "name": "get_tuning_cards"
  }
}

Input Schema

NameRequiredDescriptionDefault
title_idYesTitleID (the -DEV copy or the live one).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish readOnlyHint=true, destructiveHint=false and openWorldHint=false, so safety is covered; the description adds genuinely new behavior: the call is free, and it discloses the pinning invariant that the decision model never changes a pinned knob. It also previews the return payload (current settings, last model suggestion, pinned knobs). It does not mention output size or pagination for what could be a large catalogue.

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 verb and resource, and the parenthetical list efficiently conveys scope in few words. The middle sentence is long and carries several clauses (current state, last suggestion, pinning rule), which makes it dense but not padded; every clause carries information.

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 burden of explaining returns, and it does: it states that settings, the last model suggestion, and pinned knobs are included. Combined with the downstream hand-off to tuning_preview / tuning_apply, an agent has everything needed to call it and use the result.

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?

With a single parameter at 100% schema description coverage, the schema already explains title_id fully. The description only implicitly refers to "this Title" and adds no format, default, or validation detail beyond the schema, so the baseline of 3 applies.

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?

Opens with a specific verb and resource ("List the Title's tuning knobs") and then enumerates exactly what a knob covers (colour themes, backgrounds, motion, sound sets, feel, rewards, quests, shop, texts). It also names the sibling tools that consume its output (tuning_preview / tuning_apply), so an agent can place it in the workflow without opening any schema.

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 final sentence gives clear operational guidance: use the knob and option ids returned here as `picks` in tuning_preview / tuning_apply, which establishes this as the prerequisite read step. "Free" signals there is no cost barrier to calling it. It stops short of explicit when-not guidance or a comparison against tuning_surprise / tuning_reroll, which keeps it from a 5.

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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