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delimit_review

Get cross-model feedback on code changes before merging by running a multi-model review on a diff or file, with optional PR comment generation.

Instructions

Run a multi-model code review on a diff or file.

When to use: to get cross-model feedback on a code change before merging, optionally posted as a PR comment. When NOT to use: for structured cross-lens audit (use delimit_audit) or full multi-round debate (delimit_deliberate).

Sibling contrast: delimit_audit is structured (security / correctness / governance lenses); delimit_deliberate is full debate; this is single-prompt multi-model review.

Side effects: calls multiple models via ai.multi_review. May write a saved review record. When pr_url is provided, the review can be posted as a PR comment by the caller (this tool returns the comment body, it does not auto-post).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
diffNoGit diff or code text to review. Takes priority over file_path.
file_pathNoPath to file to review (reads current content if no diff).
contextNoAdditional context about the change.
pr_urlNoGitHub PR URL for linking the review.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so description carries full burden. Discloses side effects: calls ai.multi_review, may write saved record, returns comment body (no auto-post). Could mention failure modes or permissions, but sufficient.

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?

Well-structured: first sentence states purpose, then bullet-like sections for usage, sibling contrast, side effects. Concise and front-loaded.

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

Completeness4/5

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

Covers purpose, usage, side effects, and parameter context. Output schema exists, so return details not needed. Lacks prerequisites or error handling, but adequate for a review tool.

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%, baseline 3. Description adds value by clarifying that diff takes priority over file_path, which is not in schema.

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 ('Run a multi-model code review') and the resource ('a diff or file'). It distinguishes from siblings by naming delimit_audit for structured lenses and delimit_deliberate for debate.

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?

Includes explicit 'When to use' and 'When NOT to use' sections with clear alternative tools (delimit_audit, delimit_deliberate) and context for PR comments.

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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