Skip to main content

💡 Understanding Model Context Protocol (MCP)

Purpose: This document explains the fundamental concepts behind MCP and how the ADR Analysis Server leverages these concepts for architectural analysis.

🧠 What is Model Context Protocol?​

Model Context Protocol (MCP) is a standardized way for AI assistants to interact with external tools, data sources, and services. Think of it as a "universal translator" that allows AI models to:

  • Access Real Data - Read files, databases, APIs, and other external sources
  • Execute Actions - Run commands, modify files, trigger workflows
  • Maintain Context - Remember information across conversations and sessions
  • Extend Capabilities - Add specialized skills beyond what the AI was trained on

The Problem MCP Solves​

Before MCP, AI assistants were limited to:

  • ❌ Only information from their training data (which becomes outdated)
  • ❌ No ability to access real-time data or current files
  • ❌ No way to take actions in the real world
  • ❌ Each integration required custom, one-off solutions

With MCP, AI assistants can:

  • ✅ Access current, real-time information
  • ✅ Interact with your actual project files and data
  • ✅ Execute specialized analysis and automation tools
  • ✅ Use standardized, reusable integrations

🏗️ MCP Architecture​

Key Components​

  1. MCP Client - The application that connects your AI assistant to MCP servers
  2. MCP Protocol - Standardized communication format (JSON-RPC)
  3. MCP Server - Specialized service that provides tools and data (like our ADR Analysis Server)
  4. Tools - Functions the AI can call to perform specific tasks
  5. Resources - Dynamic content the AI can access (files, data, etc.)
  6. Prompts - Templates that help the AI understand how to use the tools effectively

🛠️ How Tools Work​

Tool Execution Flow​

Tool Categories in ADR Analysis Server​

Analysis Tools (Understanding)​

  • analyze_project_ecosystem - Comprehensive project analysis
  • discover_existing_adrs - Find and catalog existing decisions
  • analyze_content_security - Scan for sensitive information

Generation Tools (Creating)​

  • generate_adrs_from_prd - Create ADRs from requirements
  • generate_adr_todo - Extract implementation tasks
  • suggest_adrs - Recommend missing decisions

Validation Tools (Checking)​

  • compare_adr_progress - Track implementation progress
  • validate_rules - Check code compliance
  • deployment_readiness - Verify deployment preparation

Management Tools (Organizing)​

  • manage_cache - Handle server cache
  • smart_git_push - Secure version control
  • troubleshoot_guided_workflow - Systematic problem solving

📚 Resources: Dynamic Content Access​

Resources in MCP are like "live documents" that the AI can read. Unlike static files, resources are generated dynamically based on your current project state.

Our Key Resources​

Architectural Knowledge Graph​

adr://architectural_knowledge_graph?projectPath=/your/project

A comprehensive map of your project's:

  • Technology stack and dependencies
  • Architectural patterns and designs
  • Decision relationships and impacts
  • Implementation status and progress

Analysis Report​

adr://analysis_report?projectPath=/your/project&focusAreas=security,performance

Real-time analysis including:

  • Current architectural state
  • Identified issues and risks
  • Recommendations and next steps
  • Progress metrics and trends

ADR List​

adr://adr_list?adrDirectory=./adrs

Live catalog of architectural decisions:

  • All current ADRs with metadata
  • Decision status and implementation progress
  • Cross-references and dependencies
  • Search and filtering capabilities

Why Resources Matter​

Resources enable the AI to:

  • Stay Current - Always work with up-to-date project information
  • Understand Context - See the full picture of your architecture
  • Make Connections - Identify relationships between decisions and code
  • Track Progress - Monitor changes and implementation status

💭 Prompts: AI Guidance Templates​

Prompts in MCP are specialized templates that help the AI understand how to use tools effectively for specific tasks.

Prompt Categories​

Analysis Prompts​

Help the AI conduct thorough architectural analysis:

  • Project ecosystem evaluation templates
  • Security assessment guidelines
  • Performance analysis frameworks

Generation Prompts​

Guide the AI in creating high-quality content:

  • ADR writing standards and templates
  • Documentation structure patterns
  • Code generation guidelines

Validation Prompts​

Ensure the AI performs comprehensive checks:

  • Deployment readiness checklists
  • Rule compliance verification
  • Progress tracking methodologies

How Prompts Enhance AI Performance​

Without prompts, AI might:

  • Miss important architectural considerations
  • Generate inconsistent documentation formats
  • Overlook security or compliance requirements
  • Fail to follow established best practices

With specialized prompts, AI:

  • ✅ Follows proven architectural analysis methodologies
  • ✅ Generates consistent, professional documentation
  • ✅ Applies comprehensive security and compliance checks
  • ✅ Adheres to industry standards and best practices

🔄 The AI-MCP Workflow​

Typical Analysis Session​

  1. Initial Discovery

    AI asks: "What kind of project are we working with?"
    → Calls analyze_project_ecosystem
    → Gets comprehensive project understanding

  2. Context Building

    AI reads: adr://architectural_knowledge_graph
    → Understands existing decisions and patterns
    → Identifies relationships and dependencies

  3. Gap Analysis

    AI calls: suggest_adrs
    → Identifies missing architectural decisions
    → Prioritizes based on project needs and risks

  4. Documentation Generation

    AI calls: generate_adr_from_decision
    → Creates professional ADR documents
    → Follows established templates and standards

  5. Implementation Planning

    AI calls: generate_adr_todo
    → Extracts actionable implementation tasks
    → Creates prioritized development roadmap

  6. Progress Tracking

    AI calls: compare_adr_progress
    → Monitors implementation against decisions
    → Identifies blockers and next steps

Benefits of This Workflow​

  • Systematic - Follows proven architectural analysis methodologies
  • Consistent - Uses standardized templates and formats
  • Comprehensive - Covers all aspects of architectural decision-making
  • Actionable - Produces concrete next steps and implementation plans
  • Traceable - Maintains clear connection between decisions and implementation

🎯 Why MCP ADR Analysis Server is Powerful​

Traditional Approach (Without MCP)​

You → Generic AI → Generic responses based on training data
  • Limited to AI's training knowledge
  • No access to your actual project
  • Generic advice that may not apply
  • No ability to generate actual files or track progress

MCP-Enhanced Approach​

You → AI + MCP → Specialized tools → Your actual project → Tailored analysis
  • Works with your real project files and structure
  • Applies specialized architectural analysis techniques
  • Generates actual ADR documents and implementation plans
  • Tracks real progress and provides ongoing guidance

Key Advantages​

  1. Real-Time Analysis - Always works with current project state
  2. Specialized Knowledge - Applies architectural best practices and methodologies
  3. Actionable Outputs - Generates actual files, documentation, and plans
  4. Continuous Learning - Builds knowledge graph that improves over time
  5. Integration-Ready - Works with your existing tools and workflows

🚀 Advanced MCP Concepts​

Conversational Context​

MCP enables AI to maintain context across multiple interactions:

{
  "conversationContext": {
    "projectType": "microservices",
    "previousDecisions": ["database-selection", "api-gateway"],
    "currentPhase": "security-review",
    "constraints": ["budget-limited", "timeline-aggressive"]
  }
}

This context helps the AI:

  • Remember previous decisions and their rationale
  • Understand project constraints and priorities
  • Provide consistent recommendations across sessions
  • Build on previous analysis rather than starting fresh

Knowledge Graph Integration​

The server builds a persistent knowledge graph that captures:

This enables:

  • Learning from Experience - Each analysis improves future recommendations
  • Relationship Discovery - Understanding how decisions impact each other
  • Progress Tracking - Monitoring implementation across time
  • Pattern Recognition - Identifying recurring issues and solutions

Advanced AI Techniques​

The server employs sophisticated prompting techniques:

Automatic Prompt Engineering (APE)​

  • Generates optimized prompts for better analysis results
  • Adapts prompting strategies based on project characteristics
  • Continuously improves prompt effectiveness through feedback

Knowledge Generation​

  • Builds comprehensive understanding of project context
  • Synthesizes information from multiple sources
  • Creates structured knowledge representations

Reflexion Framework​

  • Self-corrects analysis through iterative refinement
  • Validates findings against multiple criteria
  • Improves accuracy through reflection and revision

🎓 Implications for Architecture Work​

How MCP Changes Architecture Analysis​

Before MCP:

  • Manual analysis of project structure and decisions
  • Generic architectural advice from documentation
  • Disconnected tools and processes
  • Inconsistent documentation and tracking

With MCP:

  • Automated, comprehensive project analysis
  • Tailored recommendations based on actual project state
  • Integrated workflow from analysis to implementation
  • Consistent, professional documentation and tracking

Best Practices for MCP-Enhanced Architecture Work​

  1. Start with Comprehensive Analysis

    • Use analyze_project_ecosystem to build complete understanding
    • Enable enhanced mode for maximum insight

  2. Leverage Continuous Context

    • Include conversation context in tool calls
    • Build on previous analysis rather than starting fresh

  3. Follow the Full Workflow

    • Discovery → Analysis → Decision → Documentation → Implementation → Tracking

  4. Use Specialized Tools for Specific Needs

    • Security analysis for sensitive projects
    • Deployment readiness for production systems
    • Performance analysis for high-scale applications

  5. Maintain the Knowledge Graph

    • Regular analysis updates keep the knowledge current
    • Progressive refinement improves accuracy over time

🔮 The Future of AI-Assisted Architecture​

MCP represents a fundamental shift toward AI assistants that can:

  • Work with Real Data - Not just trained knowledge
  • Take Real Actions - Generate files, run analysis, track progress
  • Maintain Real Context - Remember and build on previous work
  • Provide Real Value - Actionable insights and implementation guidance

The ADR Analysis Server demonstrates this future by providing AI assistants with:

  • Deep architectural analysis capabilities
  • Professional documentation generation
  • Implementation tracking and guidance
  • Continuous learning and improvement

This enables a new level of AI-human collaboration where the AI becomes a true architectural partner, not just a conversational interface to static knowledge.

Related Reading: