brutalist-mcp
Health Warn
- No license — Repository has no license file
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Low visibility — Only 6 GitHub stars
Code Fail
- process.env — Environment variable access in dist/index.js
- exec() — Shell command execution in node_modules/@modelcontextprotocol/sdk/dist/cjs/client/auth.js
Permissions Pass
- Permissions — No dangerous permissions requested
This tool acts as a bridge to multiple AI coding agents (Claude, Codex, Gemini) to provide multi-perspective code analysis and critiques. It coordinates these external CLIs to review your codebase, architecture, or ideas.
Security Assessment
The overall risk is Medium. By design, the server requires real file-system access to read and analyze your code. The automated scan flagged shell command execution and environment variable access. However, these are actually expected and safe in this context: the code relies on standard environment variables for configuration, and the shell execution originates from the official MCP SDK's internal authentication handler, not malicious custom code. There are no hardcoded secrets, and the tool requires no overtly dangerous system permissions.
Quality Assessment
The project is highly active, with its last push occurring today. However, it currently suffers from low community visibility with only 6 GitHub stars. A significant concern for enterprise or open-source use is the complete lack of a license file, meaning the legal terms for using, modifying, or distributing the software are strictly undefined.
Verdict
Use with caution — while the tool appears functionally safe, the lack of a software license poses legal risks, and giving external CLI tools broad file-system access always requires careful oversight.
All AIs are sycophants.
Brutalist MCP
Multi-perspective code analysis using Claude Code, Codex, and Gemini CLI agents.
Get direct, honest technical feedback on your code, architecture, and ideas before they reach production.
What It Does
The Brutalist MCP connects your AI coding assistant to three different CLI agents (Claude, Codex, Gemini), each providing independent analysis. This gives you multiple perspectives on:
- Code quality and security vulnerabilities
- Architecture decisions and scalability
- Product ideas and technical feasibility
- Research methodology and design flaws
Real file-system access. Straightforward analysis. No sugar-coating.
Quick Start
Step 1: Install a CLI Agent
You need at least one of these installed:
# Option 1: Claude Code (recommended)
npm install -g claude
# Option 2: Codex
# Install from https://github.com/openai/codex-cli
# Option 3: Gemini
npm install -g @google/gemini-cli
Step 2: Install the MCP Server
Choose your IDE:
Claude Code:
claude mcp add brutalist --scope user -- npx -y @brutalist/mcp@latest
Codex:
# Install globally once to avoid npx startup chatter
npm i -g @brutalist/mcp
# Add MCP using the installed binary (clean stdio)
codex mcp add brutalist -- brutalist-mcp
Configuring tool_timeout_sec for Codex:
The tool_timeout_sec parameter (defaulting to 60 seconds) for your Brutalist MCP server needs to be configured directly in your Codex configuration file at ~/.codex/config.toml. It cannot be passed via the codex mcp add command directly.
To set a custom timeout (e.g., 5 minutes or 300 seconds), add or modify the [mcp_servers.brutalist] section in ~/.codex/config.toml as follows:
[mcp_servers.brutalist]
command = "brutalist-mcp" # Ensure this matches your installation command
args = [] # Depending on your setup, this might be empty or contain arguments
tool_timeout_sec = 300 # Set your desired timeout in seconds
Cursor:
Add to ~/.cursor/mcp.json:
{
"mcpServers": {
"brutalist": {
"command": "npx",
"args": ["-y", "@brutalist/mcp@latest"]
}
}
}
VS Code / Cline:
code --add-mcp '{"name":"brutalist","command":"npx","args":["-y","@brutalist/mcp@latest"]}'
Windsurf:
Add to ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"brutalist": {
"command": "npx",
"args": ["-y", "@brutalist/mcp@latest"]
}
}
}
Step 3: Verify Installation
# Check which CLI agents are available
cli_agent_roster()
Usage Examples
Analyze Your Codebase
# Analyze entire project
roast_codebase "/path/to/your/project"
# Analyze specific modules
roast_codebase "/src/auth"
roast_codebase "/src/api/handlers"
Validate Ideas
# Evaluate a product concept
roast_idea "A social network for developers to share code snippets"
# Review technical decisions
roast_idea "Migrating our monolith to microservices with Kubernetes"
Review Architecture
# System architecture analysis
roast_architecture "Microservices with event sourcing and CQRS"
# Infrastructure design review
roast_architecture """
API Gateway → Load Balancer → 3 Node.js services → PostgreSQL
Redis for caching, Docker containers on AWS ECS
"""
Security Analysis
# Authentication review
roast_security "JWT tokens with user roles in localStorage"
# API security check
roast_security "GraphQL API with dynamic queries and no rate limiting"
Compare Perspectives
# Get multiple viewpoints on technical decisions
roast_cli_debate "Should we use TypeScript or Go for this API?"
# Compare architecture approaches
roast_cli_debate "Microservices vs Monolith for our e-commerce platform"
How It Works
This MCP server coordinates analysis from locally installed CLI agents:
- Claude Code CLI - Code review and architectural analysis
- Codex CLI - Security and technical implementation review
- Gemini CLI - System design and scalability analysis
Each agent runs locally with direct file-system access, providing independent perspectives on your code and design decisions.
Analysis time: Up to 25 minutes for complex projects. Thorough analysis requires time to examine code patterns, dependencies, and architectural decisions.
Pagination for Large Results
For analyses that exceed your IDE's token limit:
# Set chunk size for large codebases
roast_codebase({targetPath: "/monorepo", limit: 20000})
# Continue from where you left off
roast_codebase({targetPath: "/monorepo", offset: 20000, limit: 20000})
# Use cursor-based navigation
roast_codebase({targetPath: "/complex-system", cursor: "offset:25000"})
Features:
- Smart boundary detection (preserves paragraphs and sentences)
- Token estimation (~4 chars = 1 token)
- Progress indicators
- Configurable chunk size (1K to 100K characters)
Tools
Code & Architecture
| Tool | Analyzes |
|---|---|
roast_codebase |
Security vulnerabilities, performance issues, code quality |
roast_file_structure |
Directory organization, naming conventions, structure |
roast_dependencies |
Version conflicts, security vulnerabilities, compatibility |
roast_git_history |
Commit quality, branching strategy, collaboration patterns |
roast_test_coverage |
Test coverage, quality gaps, testing strategy |
Design & Planning
| Tool | Analyzes |
|---|---|
roast_idea |
Feasibility, market fit, implementation challenges |
roast_architecture |
Scalability, cost, operational complexity |
roast_research |
Methodology, reproducibility, statistical validity |
roast_security |
Attack vectors, authentication, authorization |
roast_product |
UX, adoption barriers, user needs |
roast_infrastructure |
Reliability, scaling, operational overhead |
Utilities
| Tool | Purpose |
|---|---|
roast |
Unified tool - use domain parameter to select analysis type |
brutalist_discover |
Find the best tool for your intent using natural language |
roast_cli_debate |
Multi-agent discussion from different perspectives |
cli_agent_roster |
Show available CLI agents on your system |
Tip: Use the unified
roasttool with a domain parameter for a leaner schema, or usebrutalist_discoverto find the right tool based on your intent.
See docs/pagination.md for detailed pagination documentation.
Advanced Usage
Choose Specific CLI Agents
# Use specific agents (subset selection)
roast(domain="codebase", target="/src", clis=["claude", "gemini"])
# Use a single agent
roast(domain="codebase", target="/src", clis=["claude"])
# Multi-agent analysis (default - all available)
roast(domain="idea", target="...") # All available agents provide perspectives
Agent Strengths
Different agents have different strengths:
- Code review: Claude, Codex, Gemini
- Architecture: Gemini, Claude, Codex
- Security: Codex, Claude, Gemini
- Research: Claude, Gemini, Codex
Why Multiple Perspectives
Each CLI agent brings a different approach to analysis:
- Different training data and focus areas
- Independent evaluation of the same code
- Varied perspectives on technical tradeoffs
Getting multiple viewpoints helps identify issues that a single perspective might miss.
License: MIT
Issues: https://github.com/ejmockler/brutalist-mcp/issues
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