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arbiter_apply

Resolve live code overlaps by auto-applying safe merges after adversarial verification, with writes to your worktree for trivial conflicts.

Instructions

Resolve live code overlaps and AUTO-APPLY the safe tier. Kawa judges → adversarially verifies → and, for the trivial tier only (high-confidence single-range merge that passes verify), writes the merge to your worktree, records a decision, and republishes. Writes happen ONLY in an agent-owned worktree (a linked git worktree); on a human checkout — or when a peer holds the file-set lock — it behaves like arbiter_resolve (suggest-only, no writes). Returns per-overlap outcomes { tier, applied, announcement, verifyIssue?, verdict }. Call it when you are ready to incorporate the result, then RE-READ any file it applied to (it changed on disk). For surfaced (not-applied) overlaps, use get_resolution_context to see the peer code and resolve manually.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentIdNoActive intent ID (advisory; the auto-resolution decision is recorded under it).
overlapsYesThe overlaps to resolve — each { peerUid, filePath, ranges } from the Stop collision report.
repoPathYesLocal path to the repository root
forkAuthorNoFork attribution; usually resolved by Muninn automatically — pass only for override / testing.
repoOriginNoGit remote origin URL. Auto-detected from repoPath via git if not provided.
workspaceIdNoWorkspace identifier; usually resolved by Muninn automatically — pass only for override / testing.
Behavior4/5

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

Without annotations, the description fully explains the conditional write behavior (trivial tier, agent-owned worktree), records decisions, and warns about file changes. It could add authorization or rate-limit details, but current coverage is strong.

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 front-loaded with the key action and uses bullet-like structure for outcomes. It is slightly lengthy but each sentence adds value, so it remains clear and efficient.

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 the complexity (6 params, nested objects, no output schema), the description covers conditional behavior, return format, and post-invocation steps. It is comprehensive for an AI agent to select and use the tool correctly.

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 100%, but the description adds meaning: it explains that overlaps come from the Stop collision report, and that forkAuthor and workspaceId are usually auto-resolved. This supplements the schema effectively.

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 verb 'Resolve' and resource 'live code overlaps', specifies the auto-apply behavior for the trivial tier, and distinguishes it from arbiter_resolve by detailing when it writes vs. suggests.

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

Usage Guidelines5/5

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

The description gives explicit guidance: 'Call it when you are ready to incorporate the result' and instructs to re-read applied files. It also directs users to get_resolution_context for unapplied overlaps, providing clear alternatives.

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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