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Fuck-U-Code

Community
Let bad code have nowhere to hide – assess the legacy-mess level of your code and output a beautiful report.
Category
Dev Tools #308 of 438
Stars
★ 7.3k Very popular
Transport
stdio (local process)
Runtime
Node.js
Credentials
Optional API key
License
MIT
Last commit
Tools
2
45FMRS · D

fuck-u-code is a feature-rich code quality analysis tool with multi-language and AI review support. It is well-suited for developers who enjoy a humorous tone and want quick insights into code quality. However, privacy risks from AI review and its informal nature may not fit all teams.

Strongest · Setup experience 12/20 Weakest · Reliability 5/20

Reliability
5/20
Security and permissions
7/20
Maintenance
10/20
Documentation
11/20
Setup experience
12/20
Why each score
Reliability 5/20
The review is static and based only on the supplied README and repository metadata; no server source, manifest, tests, or CI evidence was provided. The README claims two MCP tools (analyze and ai-review) and documents MCP installation, but these paths cannot be verified. The npx example uses the package name eff-u-code-mcp while installation uses eff-u-code, creating a possible package/command inconsistency. Because there is no reproducible execution evidence and the static calibration forbids a score above 12, reliability is scored 5.
Security and permissions 7/20
The README shows no malicious behavior or real secret examples, and it clearly separates offline local analysis from AI review requiring an external API, which is positive. However, without the server source, we cannot confirm filesystem access boundaries, protection of API keys in config files, confirmation mechanisms for MCP tools, or whether data-sending scope is controlled. The uninstall command removes global config, MCP entries, and the npm package, but no explicit confirmation flow for dangerous operations is evident. Deductions are made for incomplete least-privilege, confirmation, and scoping disclosure; score is 7.
Maintenance 10/20
The repository is not archived and has an MIT license. Stars are a discovery signal and do not add points. The README provides update and uninstall commands, suggesting ongoing maintenance. However, there is no commit history, release history, dependency update policy, or security-response channel, leaving governance and versioning gaps. This matches the anchor of 'active but with governance/versioning gaps', so the score is 10.
Documentation 11/20
The README covers installation, CLI usage, AI configuration, config-file examples, MCP installation, and client JSON examples with a clear structure. However, specific MCP tool parameters, call limits, error handling, and troubleshooting are missing. There is no layered MCP documentation and no source-level evidence to substantiate the claims. Documentation is usable but has hidden assumptions and troubleshooting gaps, hence 11.
Setup experience 12/20
The README provides a straightforward path: global installation, interactive mcp-install, JSON examples for Claude and Cursor, and an npx no-global-install variant. These are good setup examples. However, because no source or runtime verification was supplied, the static calibration caps setup at 15; additionally, the package-name discrepancy between eff-u-code and eff-u-code-mcp and platform-specific details are not clarified. Therefore setup is scored 12.

Static review · not runListed 2026-08-07

Read the FMRS scoring method →

Fit and risk

What it can accessReads local filesUses the network

Best for

  • Developers and teams wanting a quick snapshot of their codebase health.
  • Users of AI coding assistants (like Claude Code) who want automated code review.
  • Projects needing multi-language support (14 languages).
  • Developers who appreciate witty and blunt feedback.

Not for

  • Teams requiring serious, professional, and emotionless code reviews.
  • Enterprise users needing deeply customizable analysis rules (e.g., custom metrics).
  • Organizations managing large-scale codebases that need more powerful enterprise tools.
  • Environments where profanity or informal tone is inappropriate.

Required permissions

  • File system access to read code files for analysis.
  • Optional: Access to external AI APIs (e.g., OpenAI, Anthropic) for AI review.
  • Write access to configuration files (~/.fuckucoderc.json) and possibly MCP config files (e.g., .mcp.json).
  • Network access for updates or external API calls.

Risks and side effects

  • AI review features send code snippets to external services (e.g., OpenAI), which may raise privacy concerns.
  • Analysis results may include false positives or missed issues, so should not be the sole quality metric.
  • Tool name and feedback style may be unprofessional for formal settings.
  • Misconfiguration can prevent the MCP server from starting.

Setup

Before you start

Runtime:Node.js

OPENAI_API_KEY optionalsecret API key for OpenAI or compatible providers, needed only for ai-review with the openai provider; obtain it from the OpenAI platform.
ANTHROPIC_API_KEY optionalsecret Anthropic API key, needed only for ai-review with the anthropic provider; obtain it from the Anthropic console.
DEEPSEEK_API_KEY optionalsecret DeepSeek API key, needed only for ai-review with the deepseek provider; obtain it from the DeepSeek platform.
GEMINI_API_KEY optionalsecret Gemini API key, needed only for ai-review with the gemini provider; obtain it from Google AI Studio.
Other optional settings (3)
OPENAI_BASE_URL optional Optional custom OpenAI-compatible API endpoint.
OPENAI_MODEL optional Optional default model name.
OLLAMA_HOST optional Optional local Ollama host address, set when using the ollama provider for local review.

Install globally: npm install -g eff-u-code. Then configure MCP automatically: fuck-u-code mcp-install (interactive) or specify client: fuck-u-code mcp-install claude / cursor. For manual configuration, add an mcpServers entry to your client config file. For example, in Claude Code's ~/.claude.json or project .mcp.json, add {"mcpServers":{"fuck-u-code":{"command":"fuck-u-code-mcp"}}}. Alternatively, you can use npx: set command to "npx" and args to ["-y", "eff-u-code-mcp"].

claude_desktop_config.json
{
  "mcpServers": {
    "fuck-u-code": {
      "command": "fuck-u-code-mcp"
    }
  }
}

Shown for Claude Desktop. Other clients may use a different file or key (VS Code uses "servers") — the configurator below converts it.

.vscode/mcp.json
{
  "servers": {
    "fuck-u-code": {
      "command": "fuck-u-code-mcp"
    }
  }
}

Goes in your project's .vscode/mcp.json (VS Code uses a "servers" key).

Terminal
claude mcp add fuck-u-code -- fuck-u-code-mcp

Run it in a terminal; replace any <…> placeholders with your own values first.

Check that it works

The client's tool list should show analyze and ai-review; issuing a request like 'analyze the code quality of the current directory' and receiving a scored report confirms the connection works.

Troubleshooting

  1. Ensure eff-u-code is installed globally (npm install -g eff-u-code).
  2. Run `fuck-u-code --version` to verify installation.
  3. Run `fuck-u-code mcp-install` to reconfigure the MCP server.
  4. Check that the configuration file (~/.fuckucoderc.json) has no syntax errors.
  5. Try the npx approach: set command to "npx" and args to ["-y", "eff-u-code-mcp"].
  6. Use `fuck-u-code config show` to view current configuration.

Things to try

Once connected, you can ask your AI assistant things like:

  • Analyze the code quality of the current project and give me a score report
  • Show the 10 worst files in this project by quality score
  • Run an AI code review on the 5 lowest-scoring files
  • Output the analysis report in Chinese

Tools 2

analyze read-only
Analyze code quality and generate a score report.
ai-review read-only
Run AI-powered code review on the worst-scoring files.

Use cases

Quickly assess code quality across a repository to identify files needing improvement.
Integrate into CI to automatically generate code quality reports.
Invoke from IDE or AI tools for on-the-fly code review.
Use AI models to perform in-depth review of worst-scoring files and get improvement suggestions.

Supported clients

Claude Desktop

Listed from the project's documentation, not tested by this site.

Overview

fuck-u-code is a code quality analysis tool designed to expose shitty code quality with sharp but humorous feedback. It uses AST parsing via tree-sitter for accurate syntax analysis, supports 14 programming languages, and performs seven quality checks: complexity, size, comments, error handling, naming, duplication, and structure. Provides an overall score from 0 to 100 (higher is better) and a per-file Shit-Gas Index (higher is worse). Supports AI code review with OpenAI-compatible, Anthropic, DeepSeek, Gemini, and Ollama providers, multiple output formats (colored terminal, Markdown, JSON, HTML), and i18n (English, Chinese, Russian). As an MCP server, it can be invoked by AI tools like Claude Code, Cursor, Windsurf, etc. Code analysis runs fully offline, so your code never leaves your machine.

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Source revision 27ae48f50816 Data synced 2026-10-11 Read the FMRS scoring method