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LCV-Ideas-Software

cross-review

by LCV-Ideas-Software
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Run Until Unanimous

run_until_unanimous

Iterate AI peer-review rounds across multiple models until unanimous approval or max rounds, with caller evidence automatically persisted and transported.

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, Kimi K3 via Perplexity and DeepSeek, and xhigh on Grok 4.6; older GPT-5 families use their own ceilings).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changedv4.4.13
    • changedInput schema / properties / reasoning_effort_overrides / description
      Previous value: -"Optional 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)."New value: +"Optional 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, Kimi K3 via Perplexity and DeepSeek, and xhigh on Grok 4.6; older GPT-5 families use their own ceilings)."
  2. Changed10 schema fields changedv4.4.9
    • addedInput schema / properties / allow_auto_extension
      Added value: +{
+  "default": false,
+  "description": "Opt in to at most two evidence-only auto-extensions. False keeps max_rounds rigid.",
+  "type": "boolean"
+}
    • addedInput schema / properties / evidence / description
      Added value: +"Raw 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."
    • addedInput schema / properties / max_rounds / description
      Added value: +"Hard review-round ceiling unless allow_auto_extension is explicitly true."
    • changedInput schema / properties / reasoning_effort_overrides / description
      Previous value: -"Optional 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."New value: +"Optional 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)."
    • changedInput schema / properties / reasoning_effort_overrides / properties / claude / enum
      Previous value: -[
-  "none",
-  "minimal",
-  "low",
-  "medium",
-  "high",
-  "xhigh",
-  "max"
-]New value: +[
+  "none",
+  "minimal",
+  "low",
+  "medium",
+  "high",
+  "xhigh",
+  "max",
+  "ultra"
+]
    • changedInput schema / properties / reasoning_effort_overrides / properties / codex / enum
      Previous value: -[
-  "none",
-  "minimal",
-  "low",
-  "medium",
-  "high",
-  "xhigh",
-  "max"
-]New value: +[
+  "none",
+  "minimal",
+  "low",
+  "medium",
+  "high",
+  "xhigh",
+  "max",
+  "ultra"
+]
    • changedInput schema / properties / reasoning_effort_overrides / properties / deepseek / enum
      Previous value: -[
-  "none",
-  "minimal",
-  "low",
-  "medium",
-  "high",
-  "xhigh",
-  "max"
-]New value: +[
+  "none",
+  "minimal",
+  "low",
+  "medium",
+  "high",
+  "xhigh",
+  "max",
+  "ultra"
+]
    • changedInput schema / properties / reasoning_effort_overrides / properties / gemini / enum
      Previous value: -[
-  "none",
-  "minimal",
-  "low",
-  "medium",
-  "high",
-  "xhigh",
-  "max"
-]New value: +[
+  "none",
+  "minimal",
+  "low",
+  "medium",
+  "high",
+  "xhigh",
+  "max",
+  "ultra"
+]
    • changedInput schema / properties / reasoning_effort_overrides / properties / grok / enum
      Previous value: -[
-  "none",
-  "minimal",
-  "low",
-  "medium",
-  "high",
-  "xhigh",
-  "max"
-]New value: +[
+  "none",
+  "minimal",
+  "low",
+  "medium",
+  "high",
+  "xhigh",
+  "max",
+  "ultra"
+]
    • changedInput schema / properties / reasoning_effort_overrides / properties / perplexity / enum
      Previous value: -[
-  "none",
-  "minimal",
-  "low",
-  "medium",
-  "high",
-  "xhigh",
-  "max"
-]New value: +[
+  "none",
+  "minimal",
+  "low",
+  "medium",
+  "high",
+  "xhigh",
+  "max",
+  "ultra"
+]
  3. First observedv4.4.8

TDQS

A3.7/5.0
Behavior5/5

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

Beyond the annotations, the description discloses the termination condition, durable evidence persistence, automatic transport, relator lottery behavior, server rejection of lead_peer===caller with the exact error code, and the workspace hard gate. This substantially enriches what the structured annotations alone 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?

The description is front-loaded with the core behavior, then adds evidence handling and versioned relator-lottery semantics. Every sentence carries unique behavioral information; the density is justified by the complexity of the tool.

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 14-parameter tool with no output schema, the description covers the core loop, evidence handling, and relator gate, which is strong. However it omits return/polling behavior, cost controls, mode/peer selection semantics, and terminal outcome representation, leaving gaps that an agent must resolve elsewhere.

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 only 36% schema description coverage, the tool text correctly explains the most consequential parameters (caller, lead_peer, evidence) but leaves mode, peers, max_cost_usd, initial_draft, until_stopped, response_format, and other parameters to be inferred from enums/defaults. It compensates for key protocol params but not for the full parameter set.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb-resource statement: 'Generate or revise a draft and continue real API peer-review rounds until unanimous READY or the configured max_rounds is reached.' This clearly identifies the purpose, but it never names or contrasts sibling tools such as session_start_unanimous, so the agent must infer the differentiation.

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

Usage Guidelines2/5

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

There is no explicit when-to-use or when-not-to-use guidance relative to the many session_* and ask_peers siblings. The nearest guidance is the conditional explanation of caller/lead_peer and the note that evidence needs no manual attachment, but that addresses parameter workflow rather than tool selection.

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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