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khoa-na

antigravity-mcp

by khoa-na

review-diff

Automatically review uncommitted or staged Git changes for security, performance, and logic issues. Perform structured code review in read-only mode, so repository files remain unchanged.

Instructions

Perform an automated, structured code review on current uncommitted or staged git changes. Runs in strictly read-only mode so it never modifies your repository files.

Parameters:

  • workspace: Path to the git repository (default: current workspace).

  • staged: If true, reviews only staged changes (git diff --staged). Otherwise reviews all uncommitted changes.

  • focus: Optional focus area (e.g. 'security', 'performance', 'logic bugs', 'code style').

  • model: Target model ID (default: gemini-3.7-flash-high).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
focusNo
modelNo
stagedNo
workspaceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. First observedv2.0.0

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are present, so the description carries the burden. It explicitly discloses 'strictly read-only mode' and that it 'never modifies your repository files', which is important safety-relevant behavior. It does not mention other side effects such as external model calls or quota usage, but the core non-destructive behavior is clearly stated.

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?

Front-loaded purpose, followed by a single behavioral guarantee, then a clean, scannable parameter list. Every sentence contributes needed information and there is no filler.

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?

An output schema exists, so return-value details are not required. All parameters are documented with defaults and the diff scope is clear. The only minor gap is that available model IDs are not enumerated, but the model is optional and has a default, and list-models exists as a sibling.

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

Parameters5/5

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

Schema description coverage is 0%, btut the descriprtion compensates fully by explaining all four parameters: workspace path with default, staged boolean with diff semantics, focus with concrete examples, and model with default ID. This is exactly the semantic layer the raw schema lacks.

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 specific verb and resource: 'Perform an automated, structured code review' on 'current uncommitted or staged git changes'. This clearly separates it from conversation-oriented siblings like ask-gemini and ask-antigravity, and from generate-tests, which generates tests rather than reviewing diffs.

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?

Provides clear context: the tool reviews uncommitted or staged changes, with the staged param deciding which diff to examine. It does not explicitly name alternatives or state when not to use it, so not a 5, but an agent can infer the intended sitatuation reliably.

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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