# antigravity-review-mcp

> Use this tool when you need to automate code reviews and improve code quality. It solves problems of manual review inefficiencies and provides focused feedback by analyzing git diffs and source context. The tool takes in git diffs and source code as input and outputs targeted code review suggestions, making it ideal for use in software development workflows.

Canonical page: https://skillsregistry.net/skills/enferlain-review-mcp  
JSON: https://api.skillsregistry.net/v1/skills/enferlain-review-mcp

## Description

AI-powered code review using Zhipu GLM. It gathers git diffs and source context to provide focused code reviews.

## Trust

- **Trust score (0–1):** 0.67
- **Verification tier:** scanned
- **Last scanned:** 2026-08-31

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** version-control
- **Updated:** 2026-08-31

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/mn3kaafxlb)
- **Repository:** <https://github.com/Enferlain/review-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "enferlain-review-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/enferlain-review-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/enferlain-review-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt