ctroy-code-analysis
An MCP server that provides six code review tools for use with Claude Code or any MCP client. Each tool reads a file from disk, pairs its contents with a structured review prompt, and returns the bundle for the LLM to evaluate.
README
ctroy-code-analysis
An MCP server that provides six code review tools for use with Claude Code (or any MCP client). Each reviewer reads a file from disk, pairs its contents with a structured review prompt, and returns the bundle for the LLM to evaluate.
Installation
pip install ctroy-code-analysis
Requires Python 3.10+.
Connecting to Claude Code
Add the server to your project's .mcp.json (per-project) or
~/.claude.json (global):
{
"mcpServers": {
"ctroy-code-analysis": {
"command": "ctroy-code-analysis"
}
}
}
Restart Claude Code. The six tools will appear automatically.
Tools
Each tool takes a filepath (or directory), reads the contents, and returns them alongside review instructions. The LLM then generates the review.
| Tool | Input | What It Reviews |
|---|---|---|
review_comments |
filepath |
Identifies superfluous comments that restate obvious code and inaccurate comments that contradict what the code does. |
review_names |
filepath |
Identifies unclear, inaccurate, or shadowed names in classes, functions, variables, and constants. |
review_cohesion |
filepath |
Identifies related logic scattered across the codebase that should be colocated. |
review_performance |
filepath |
Identifies unnecessary computation, inefficient algorithms, and missed optimization opportunities. |
review_test_coverage |
filepath |
Identifies untested code paths including edge cases, error paths, and boundary conditions. |
draw_gridmat |
directory |
Lists the directory structure and produces an ASCII execution-path diagram with emoji-coded entry points. |
Example usage in Claude Code
Ask Claude naturally:
Use review_comments on src/parser.py
Use review_performance on lib/data_pipeline.py
Use draw_gridmat on the src/ directory
Prompts
Each tool also has a corresponding prompt (prefixed with prompt_). Prompts
return just the review instructions without reading any files, so the LLM
applies them to code it already has in context.
| Prompt | Input |
|---|---|
prompt_review_comments |
filepath |
prompt_review_names |
filepath |
prompt_review_cohesion |
filepath |
prompt_review_performance |
filepath |
prompt_review_test_coverage |
filepath |
prompt_draw_gridmat |
directory |
What the reviewers look for
Comment review flags two categories, ordered by severity: inaccurate comments (say something the code doesn't do) and superfluous comments (restate what the code clearly says).
Name review flags five categories: unclear names, inaccurate names,
shadowed variables, overloaded temporaries (tmp, i, x reused across
unrelated blocks), and low-confidence blocks where names make it hard to
reason about the code.
Cohesion review looks for ten patterns of scattered code: distant configuration, split validation, fragmented type definitions, separated tests, dispersed error handling, remote utilities, disconnected docs, scattered state management, split domain logic, and orphaned dependencies.
Performance review analyzes five categories: algorithmic complexity, data structure efficiency, unnecessary work, I/O and external operations, and language-specific optimizations. Each finding includes expected improvement and tradeoffs.
Test coverage review checks for gaps in: happy paths, branch coverage, edge cases, error paths, boundary conditions, integration points, return values, state changes, and concurrency concerns.
Gridmat picks up to 5 entry points, assigns each a color-coded emoji, and traces execution paths downward through the codebase in an ASCII box-drawing diagram.
Running the server directly
The server uses stdio transport. To start it manually:
ctroy-code-analysis
Or:
python -m ctroy_code_analysis.server
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。