💡 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❌ Only information from their training data (which becomes outdated)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

MCP Client - The application that connects your AI assistant to MCP servers
MCP Protocol - Standardized communication format (JSON-RPC)
MCP Server - Specialized service that provides tools and data (like our ADR Analysis Server)
Tools - Functions the AI can call to perform specific tasks
Resources - Dynamic content the AI can access (files, data, etc.)
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=docs/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

Initial Discovery

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

Context Building

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

Gap Analysis

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

Documentation Generation

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

Implementation Planning

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

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

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

🚀 Advanced MCP Concepts

Conversational Context

MCP enables AI to maintain context across multiple interactions:

json

{
  "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

Start with Comprehensive Analysis
- Use analyze_project_ecosystem to build complete understanding
- Enable enhanced mode for maximum insight
Leverage Continuous Context
- Include conversation context in tool calls
- Build on previous analysis rather than starting fresh
Follow the Full Workflow
- Discovery → Analysis → Decision → Documentation → Implementation → Tracking
Use Specialized Tools for Specific Needs
- Security analysis for sensitive projects
- Deployment readiness for production systems
- Performance analysis for high-scale applications
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:

Tutorial: Your First MCP Analysis - Hands-on introduction to using MCP
API Reference - Complete tool documentation
Architecture Overview - Design decisions behind the server

💡 Understanding Model Context Protocol (MCP) ​

🧠 What is Model Context Protocol? ​

The Problem MCP Solves ​

🏗️ MCP Architecture ​

Key Components ​

🛠️ How Tools Work ​

Tool Execution Flow ​

Tool Categories in ADR Analysis Server ​

Analysis Tools (Understanding) ​

Generation Tools (Creating) ​

Validation Tools (Checking) ​

Management Tools (Organizing) ​

📚 Resources: Dynamic Content Access ​

Our Key Resources ​

Architectural Knowledge Graph ​

Analysis Report ​

ADR List ​

Why Resources Matter ​

💭 Prompts: AI Guidance Templates ​

Prompt Categories ​

Analysis Prompts ​

Generation Prompts ​

Validation Prompts ​

How Prompts Enhance AI Performance ​

🔄 The AI-MCP Workflow ​

Typical Analysis Session ​

Benefits of This Workflow ​

🎯 Why MCP ADR Analysis Server is Powerful ​

Traditional Approach (Without MCP) ​

MCP-Enhanced Approach ​

Key Advantages ​

🚀 Advanced MCP Concepts ​

Conversational Context ​

Knowledge Graph Integration ​

Advanced AI Techniques ​

Automatic Prompt Engineering (APE) ​

Knowledge Generation ​

Reflexion Framework ​

🎓 Implications for Architecture Work ​

How MCP Changes Architecture Analysis ​

Best Practices for MCP-Enhanced Architecture Work ​

🔮 The Future of AI-Assisted Architecture ​