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Run Until Unanimous

run_until_unanimous

Run peer-review rounds among multiple AI models until unanimous READY or max rounds. Control lead peer selection, cost limits, and caller constraints.

Instructions

Generate or revise a draft and continue real API peer-review rounds until unanimous READY or the configured max_rounds is reached. v2.11.0: when caller is set to a peer id (claude|codex|gemini|deepseek|grok), the relator lottery activates: omit lead_peer to have the server randomly select a non-caller peer as relator (modeled on judicial colegiados), or supply an explicit lead_peer that is NOT the caller. An explicit lead_peer === caller is rejected at the server with caller_cannot_be_lead_peer — an agent never reviews itself (workspace HARD GATE).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoship
taskYes
peersNo
callerNooperator
evidenceNo
lead_peerNo
max_roundsNo
max_cost_usdNo
review_focusNoOptional provider-neutral review scope anchor. This is not Claude Code's /focus UI command; it is injected as a front-loaded Review Focus prompt block for every selected peer, including OUT OF SCOPE handling for unrelated findings.
initial_draftNo
until_stoppedNo
response_formatNojson
reasoning_effort_overridesNoOptional per-peer reasoning_effort overrides for this call. Keys are peer ids (codex|claude|gemini|deepseek|grok|perplexity); missing keys fall back to global config. Useful to dial down expensive peers (e.g. Grok grok-4.20-multi-agent xhigh = 16 agents, or Perplexity sonar-deep-research that bills citation + reasoning + search queries separately) for routine reviews without editing the host MCP configs.
Behavior5/5

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

The description reveals real API calls, stopping conditions, the relator lottery, and the hard gate preventing self-review. This adds substantial behavioral context beyond annotations (openWorldHint, non-readOnly) and describes rejection behavior for invalid lead_peer.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is informative but verbose, with version details and an extended explanation of the relator lottery. It is front-loaded with purpose, but could be more concise by focusing only on essential usage and omitting tangential details.

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?

Given the tool has 13 parameters and no output schema, the description is incomplete. It omits many parameter meanings (e.g., mode, evidence, review_focus) and does not describe return values or side effects beyond the process itself.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 15%, and the description only clarifies two parameters (caller and lead_peer) in the context of the relator lottery. The other 11 parameters (e.g., mode, task, peers, evidence) are not explained, leaving most parameters with little semantic guidance.

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 it generates or revises a draft via peer-review rounds until unanimous or max_rounds. It uses specific verbs and distinguishes itself from sibling tools like session_start_unanimous by mentioning the relator lottery feature.

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 description explains when to use the tool (for round-based peer review) and provides detailed context for the relator lottery. However, it does not explicitly mention when not to use it or point to simpler alternatives like session_start_unanimous.

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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