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Run Until Unanimous

run_until_unanimous

Run iterative AI peer-review rounds until unanimous approval or max rounds. Automatically persists evidence and supports optional lead peer selection.

Instructions

Generate or revise a draft and continue real API peer-review rounds until unanimous READY or the configured max_rounds is reached. AI evidence supplied in evidence is persisted durably and transported automatically; no manual operator attachment is required. v2.11.0: when caller is set to a peer id (claude|codex|gemini|deepseek|grok|perplexity), 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
evidenceNoRaw evidence from the authenticated AI caller. It is persisted automatically as durable, SHA-256-addressed caller_submitted_unverified material and transported to reviewers; no manual operator attachment is required. Do not call session_attach_evidence for this routine path.
lead_peerNo
max_roundsNoHard review-round ceiling unless allow_auto_extension is explicitly true.
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
allow_auto_extensionNoOpt in to at most two evidence-only auto-extensions. False keeps max_rounds rigid.
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. This is a shared scale: adapters normalize unsupported literals to the selected model's documented enum (`ultra` becomes max on GPT-5.6 and high on Grok 4.5; older GPT-5 families use their own ceilings).
Behavior4/5

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

Annotations indicate mutability (readOnlyHint=false) and openness (openWorldHint=true). The description adds behavioral context: persistence of evidence, the lottery mechanism, and the hard gate preventing self-review. This enriches understanding beyond annotations. No contradictions.

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?

The description is longer but each sentence adds value, with key details front-loaded (purpose, evidence handling, v2.11.0 lottery). While not extremely concise, it avoids verbosity and maintains focus.

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's complexity (14 parameters, nested objects, no output schema), the description covers only the core flow and a few edge cases. Missing details like mode behavior, peers configuration, and reasoning overrides leave significant gaps for the agent.

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 36%, and the description compensates minimally by explaining only evidence, caller, and lead_peer. Most parameters (mode, peers, max_cost, until_stopped, response_format, reasoning_effort_overrides) lack semantic explanation in the description, leaving the agent to interpret schema details alone.

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 generates/revises a draft and runs real API peer-review rounds until unanimous READY or max_rounds. It distinguishes from siblings like ask_peers (simple query) and session_* tools by specifying the iterative peer-review workflow. Specific version features (v2.11.0 lottery) add precision.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for reaching unanimous consensus but does not explicitly state when to use this tool versus alternatives like ask_peers or session_start_unanimous. No 'when not to use' guidance is provided, leaving the agent to infer context from sibling names.

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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