tosin2013/mcp-adr-analysis-server
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The MCP ADR Analysis Server is a comprehensive solution for AI-powered architectural decision analysis and management, providing intelligent analysis of Architectural Decision Records (ADRs), project ecosystems, and development workflows.
MCP ADR Analysis Server
AI-powered architectural analysis for intelligent development workflows. Returns actual analysis results, not prompts to submit elsewhere.
TL;DR
What: MCP server that provides AI-powered architectural decision analysis and ADR management
Who: AI coding assistants (Claude, Cline, Cursor), enterprise architects, development teams
Why: Get immediate architectural insights instead of prompts, with 95% confidence scoring
How: npm install -g mcp-adr-analysis-server
โ Configure with OpenRouter API โ Start analyzing
Key Features: Tree-sitter AST analysis โข Security content masking โข Test-driven development โข Deployment readiness validation
Author: Tosin Akinosho | Repository: GitHub
What is MCP?
The Model Context Protocol enables seamless integration between AI assistants and external tools. This server enhances AI assistants with deep architectural analysis capabilities, enabling intelligent code generation, decision tracking, and development workflow automation.
โจ Core Capabilities
๐ค AI-Powered Analysis - Immediate architectural insights with OpenRouter.ai integration ๐๏ธ Technology Detection - Identify any tech stack and architectural patterns ๐ ADR Management - Generate, suggest, and maintain Architectural Decision Records ๐ Smart Code Linking - AI-powered discovery of code files related to ADRs and decisions ๐ก๏ธ Security & Compliance - Detect and mask sensitive content automatically ๐งช TDD Integration - Two-phase Test-Driven Development with validation ๐ Deployment Readiness - Zero-tolerance test validation with hard blocking
๐
๐ฆ Quick Installation
# Global installation (recommended)
npm install -g mcp-adr-analysis-server
# RHEL 9/10 systems (special installer)
curl -sSL https://raw.githubusercontent.com/tosin2013/mcp-adr-analysis-server/main/scripts/install-rhel.sh | bash
๐ |
โก Quick Setup (3 Steps)
- Get API Key: OpenRouter.ai/keys
- Set Environment:
OPENROUTER_API_KEY=your_key
+EXECUTION_MODE=full
- Configure Client: Add to Claude Desktop, Cline, Cursor, or Windsurf
{
"mcpServers": {
"adr-analysis": {
"command": "mcp-adr-analysis-server",
"env": {
"PROJECT_PATH": "/path/to/your/project",
"OPENROUTER_API_KEY": "your_key_here",
"EXECUTION_MODE": "full"
}
}
}
}
๐ |
๐ Usage Examples
Ask Claude (or any MCP client):
"Analyze this React project's architecture and suggest ADRs for any implicit decisions"
"Generate ADRs from the PRD.md file and create a todo.md with implementation tasks"
"Check this codebase for security issues and provide masking recommendations"
The server returns actual analysis results instead of prompts to submit elsewhere!
// Basic project analysis
const analysis = await analyzeProjectEcosystem({
projectPath: '/path/to/project',
analysisType: 'comprehensive',
});
// Generate ADRs from requirements
const adrs = await generateAdrsFromPrd({
prdPath: 'docs/PRD.md',
outputDirectory: 'docs/adrs',
});
// Smart Code Linking - Find code related to ADR decisions
const relatedCode = await findRelatedCode(
'docs/adrs/001-auth-system.md',
'We will implement JWT authentication with Express middleware',
'/path/to/project',
{
useAI: true, // AI-powered keyword extraction
useRipgrep: true, // Fast text search
maxFiles: 10, // Limit results
includeContent: true, // Include file contents
}
);
๐ |
๐ฏ Use Cases
๐จโ๐ป AI Coding Assistants - Enhance Claude, Cline, Cursor with architectural intelligence
๐ฌ Conversational AI - Answer architecture questions with confidence scoring
๐ค Autonomous Agents - Continuous analysis and rule enforcement
๐ข Enterprise Teams - Portfolio analysis and migration planning
๐
๐ ๏ธ Technology Stack
Runtime: Node.js 20+ โข Language: TypeScript โข Framework: MCP SDK โข Testing: Jest (>80% coverage) Search: ripgrep + fast-glob โข AI Integration: OpenRouter.ai โข Web Research: Firecrawl โข Code Analysis: Smart Code Linking
๐
๐ Project Structure
src/tools/ # 23 MCP tools for analysis
docs/adrs/ # Architectural Decision Records
tests/ # >80% test coverage
.github/ # CI/CD automation
๐
๐งช Testing
npm test # Run all tests (>80% coverage)
npm run test:coverage # Coverage report
๐
๐ฅ Firecrawl Integration (Optional)
Enhanced web research capabilities for comprehensive architectural analysis.
# Option 1: Cloud service (recommended)
export FIRECRAWL_ENABLED="true"
export FIRECRAWL_API_KEY="fc-your-api-key-here"
# Option 2: Self-hosted
export FIRECRAWL_ENABLED="true"
export FIRECRAWL_BASE_URL="http://localhost:3000"
# Option 3: Disabled (default - server works without web search)
Benefits: Real-time research โข Enhanced ADRs โข Best practices discovery โข Intelligent web scraping
๐
๐ง Development
git clone https://github.com/tosin2013/mcp-adr-analysis-server.git
cd mcp-adr-analysis-server
npm install && npm run build && npm test
Quality Standards: TypeScript strict mode โข ESLint โข >80% test coverage โข Pre-commit hooks
๐ |
๐ง Troubleshooting
Common Issues:
- RHEL Systems: Use special installer script
- Tools return prompts: Set
EXECUTION_MODE=full
+ API key - Module not found: Run
npm install && npm run build
- Permission denied: Check file permissions and project path
๐
๐ Security & Performance
Security: Automatic secret detection โข Content masking โข Local processing โข Zero trust
Performance: Multi-level caching โข Incremental analysis โข Parallel processing โข Memory optimization
๐ |
๐ค Contributing
We welcome contributions! Fork โ Create branch โ Add tests โ Submit PR
Standards: TypeScript strict โข >80% coverage โข ESLint โข Security validation โข MCP compliance
๐
๐ Resources
Official: MCP Specification โข MCP SDK
Community: MCP Registry โข Discord
Project: โข โข
๐ License
MIT License - see file for details.
๐ Acknowledgments
- Anthropic for creating the Model Context Protocol
- The MCP Community for inspiration and best practices
- Contributors who help make this project better
Built with โค๏ธ by Tosin Akinosho for AI-driven architectural analysis
Empowering AI assistants with deep architectural intelligence and decision-making capabilities.