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select_iteration

Load an @R# onto the live canvas so mutate tools edit that iteration.

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": {
      "ref": "<ref>"
    },
    "name": "select_iteration"
  }
}

Input Schema

NameRequiredDescriptionDefault
refYes@R# to load onto the canvas (usually champion).
strategy_idNoStrategy id. Prefer always: routes to that Lab tab, and NO_ACTIVE_LAB errors include a direct Lab URL to open.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It states that the tool loads an iteration onto the live canvas and that subsequent mutate tools act on it, but it does not mention side effects like replacing the currently selected iteration, persistence, or error conditions. This is adequate but has clear gaps.

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?

One sentence, front-loaded with the core action, and the purpose clause earns its place. There is no wasted wording or redundant repetition of schema content.

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

Completeness3/5

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

For a simple 2-parameter tool with no output schema and no annotations, this is minimally sufficient: the agent knows what to load and why. However, it lacks guidance on when strategy_id matters, what happens to the previous canvas selection, and what a successful load returns or signals.

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 description coverage is 100%, so the schema already explains both 'ref' and 'strategy_id'. The description adds little parameter-level meaning beyond naming the @R# as the thing loaded; it does not elaborate on the optional strategy_id or when to use it.

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 uses a specific verb ('Load') and a specific resource ('@R# onto the live canvas'), then explains the purpose ('so mutate tools edit that iteration'). This clearly distinguishes it from the read/list/get siblings and the patch/delete mutation tools.

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 phrase 'so mutate tools edit that iteration' provides clear contextual guidance: this tool should be called before applying mutations such as patch_cooldown, patch_entry_gate, or patch_exit. It does not explicitly say when not to use it or name alternatives, so it stops short of 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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