AI Code Review MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PORT | No | The port number for the server (default: 3000) |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| review_codeB | 构建用于代码整体审查与打分的 LLM 提示词（不直接调用 LLM） |
| review_diffA | 构建用于 Git diff 变更审查与打分的 LLM 提示词（不直接调用 LLM） |
| review_fileB | 构建用于单文件审查与打分的 LLM 提示词（不直接调用 LLM） |
| parse_review_scoreA | 从审查文本中解析评分（提取 '总分:XX分' 格式） |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool targets a distinct aspect of code review: overall code review, diff review, single file review, and score parsing. There is no overlap, so an agent can clearly differentiate them.
All tools use a consistent verb_noun pattern in snake_case: review_code, review_diff, review_file, parse_review_score. The naming is predictable and follows a clear convention.
With 4 tools, the server is well-scoped for generating code review prompts and parsing scores. The count is neither too few nor excessive, matching the narrow domain perfectly.
The server covers the main prompt generation scenarios (code, diff, file) and score parsing. A minor gap is the lack of a tool to extract detailed review comments, but for prompt generation and score extraction it is sufficiently complete.