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Start Until Unanimous

session_start_unanimous

Starts background AI review rounds across multiple models to reach unanimous consensus, using supplied evidence and optional budget or round limits.

Instructions

Start real API generation/revision rounds in the background until unanimity, max_rounds or budget limit. AI evidence supplied in evidence is persisted durably and transported automatically; no manual operator attachment is required. v2.11.0: same caller + relator-lottery semantics as run_until_unanimous — see that tool for details.

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.
session_idNo
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?

Beyond annotations (readOnlyHint=false, openWorldHint=true), the description adds that rounds run in the background, evidence is persisted durably and transported automatically. This provides useful behavioral context beyond what annotations convey.

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?

Three sentences, front-loaded with purpose, followed by evidence behavior and a sibling reference. Every sentence adds value with no redundancy.

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 (15 parameters, nested objects, no output schema, many siblings), the description is too brief. It does not explain mode, peers, review_focus, initial_draft, reasoning_effort_overrides, etc., relying heavily on the schema and a reference to run_until_unanimous.

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?

With schema description coverage at 33% (low), the description only adds meaning for the evidence parameter, stating it is persisted automatically. No explanation for most of the 15 parameters, leaving a significant gap.

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 starts real API generation/revision rounds in the background until unanimity, max_rounds, or budget limit. It distinguishes from siblings by referencing run_until_unanimous and noting same caller+relator-lottery semantics.

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 references run_until_unanimous for details but does not explicitly state when to use this tool vs that alternative. It implies similarity but lacks explicit when/when-not guidance.

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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