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aiworkhub_agent_accept_review

COORDINATOR/WRITE-GATED: accept one review_ready request, phase 2.

Instructions

COORDINATOR/WRITE-GATED: accept one review_ready request, phase 2.

Re-runs scope, required-output, and validation gates against the retained isolated worktree, compares every changed path's hash against the ones recorded when the request first reached review, and only then promotes into the canonical repo and finalizes the task as done. Any precondition or revalidation failure leaves the canonical repo untouched and the task in review. Idempotent: accepting the same already-finished request again returns already_accepted instead of re-promoting. Medium-and-higher risk profiles also validate a fresh canonical+candidate combined tree and require strict reviewer findings; high/critical review requires an explicit confirm_high_risk decision.

manager_correction -- unset by default, and every other behaviour is then exactly as before -- accepts the sealed candidate PLUS a manager patch instead of rejecting it: {"patch": <unified diff>, "finding_ids": ["reviewer:<lens>:<id>", ...], "reason": str}. It lifts the refinement_required blocker of each named finding that a selected reviewer reported and aiworkhub_agent_dispose_review_finding already recorded as confirmed, provided its severity is not blocking. The patch is validated before any promotion or state change and refuses with manager_correction_invalid:<reason> when the manager is unverified, the patch does not apply to the sealed candidate bytes, a touched path is outside the card's allowed_writes or not part of the candidate, no finding is named, or a named finding is unconfirmed or blocking. The declared validations and the mechanical gate then run on the CORRECTED bytes, those are the bytes promoted, and the accepted-outcome receipt binds the patch digest with the candidate and corrected hashes.

A successful reply is the receipt (aiworkhub.accept_review_receipt.v1): promoted_paths, reviewer_finalization, acceptance_evidence_record, accepted_outcome_receipt, needfix_closure, learning_commit_owed, plus the finalized card's status and card_sha256/card_bytes. include_card = none (default) | summary | full adds the card view. Errors are unchanged.

Input Schema

NameRequiredDescriptionDefault
task_idYes
request_idYes
include_cardNonone
risk_signalsNo
reviewer_reportsNo
confirm_high_riskNo
manager_correctionNo
requested_risk_tierNo
reviewer_request_idsNo
confirm_destructive_changeNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and delivers: idempotent behavior returning already_accepted, failure semantics (canonical repo untouched, task stays in review), risk-tier validation requirements, confirm_high_risk gating, and manager_correction refusal modes with error-code shapes. Rich beyond any structured field.

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?

Front-loads the core action and scope before descending into the manager_correction branch. Dense but mostly warranted; a few clauses (e.g., the receipt field enumeration) are verbose though they do provide useful structure.

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

Completeness5/5

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

Given a mutation tool with no annotations, 10 params at 0% schema coverage, and an output schema, the description supplies the preconditions, side effects, idempotency, and error behavior an agent needs. The output schema covers returns, yet the description still names the receipt contents for clarity.

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

Parameters4/5

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

Schema coverage is 0% across 10 params, so the description must compensate. It thoroughly documents manager_correction's object shape, include_card values, and confirm_high_risk/risk-tier behavior, but leaves risk_signals, reviewer_reports, requested_risk_tier, reviewer_request_ids, and confirm_destructive_change unexplained.

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?

States a precise verb+resource+scope: 'accept one review_ready request, phase 2,' and details the promotion/finalization actions it performs. This clearly distinguishes it from siblings like aiworkhub_agent_accept_preview (phase 1) and aiworkhub_task_reject_review.

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?

Explains the precondition flow (re-run gates, compare hashes before promoting) and contrasts the manager_correction path against 'rejecting it.' However, it never explicitly names reject_review or accept_preview as alternatives with a clear 'use X when Y' rule, leaving some routing to inference.

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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