mcp-concepts.mdโข13.9 kB
# ๐ก 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.
---
## ๐ง 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:
- โ Only information from their training data (which becomes outdated)
- โ No ability to access real-time data or current files
- โ No way to take actions in the real world
- โ Each integration required custom, one-off solutions
With MCP, AI assistants can:
- โ
Access current, real-time information
- โ
Interact with your actual project files and data
- โ
Execute specialized analysis and automation tools
- โ
Use standardized, reusable integrations
---
## ๐๏ธ MCP Architecture
```mermaid
graph TB
subgraph "Your Environment"
User[๐ค You]
AI[๐ค AI Assistant<br/>Claude, ChatGPT, etc.]
end
subgraph "MCP Layer"
Client[๐ฑ MCP Client<br/>Desktop app, IDE, etc.]
Protocol[๐ MCP Protocol<br/>JSON-RPC over stdio]
end
subgraph "MCP Server"
Server[๐๏ธ ADR Analysis Server]
Tools[๐ ๏ธ Tools<br/>37 analysis functions]
Resources[๐ Resources<br/>Dynamic content]
Prompts[๐ญ Prompts<br/>AI templates]
end
subgraph "Your Project"
Files[๐ Files]
Code[๐ป Code]
Docs[๐ Documentation]
ADRs[๐ ADRs]
end
User --> AI
AI --> Client
Client --> Protocol
Protocol --> Server
Server --> Tools
Server --> Resources
Server --> Prompts
Tools --> Files
Tools --> Code
Tools --> Docs
Tools --> ADRs
```
### 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
---
## ๐ ๏ธ How Tools Work
### Tool Execution Flow
```mermaid
sequenceDiagram
participant AI as ๐ค AI Assistant
participant MCP as ๐ก MCP Protocol
participant Server as ๐๏ธ ADR Server
participant Project as ๐ Your Project
AI->>MCP: "I need to analyze this project"
MCP->>Server: tool_call: analyze_project_ecosystem
Server->>Project: Read files, analyze structure
Project-->>Server: Project data
Server->>Server: Process with AI analysis
Server-->>MCP: Analysis results
MCP-->>AI: Structured analysis data
AI-->>AI: Generate response for user
```
### 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
---
## ๐ 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
---
## ๐ญ 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:
- โ
Follows proven architectural analysis methodologies
- โ
Generates consistent, professional documentation
- โ
Applies comprehensive security and compliance checks
- โ
Adheres to industry standards and best practices
---
## ๐ The AI-MCP Workflow
### Typical Analysis Session
1. **Initial Discovery**
```
AI asks: "What kind of project are we working with?"
โ Calls analyze_project_ecosystem
โ Gets comprehensive project understanding
```
2. **Context Building**
```
AI reads: adr://architectural_knowledge_graph
โ Understands existing decisions and patterns
โ Identifies relationships and dependencies
```
3. **Gap Analysis**
```
AI calls: suggest_adrs
โ Identifies missing architectural decisions
โ Prioritizes based on project needs and risks
```
4. **Documentation Generation**
```
AI calls: generate_adr_from_decision
โ Creates professional ADR documents
โ Follows established templates and standards
```
5. **Implementation Planning**
```
AI calls: generate_adr_todo
โ Extracts actionable implementation tasks
โ Creates prioritized development roadmap
```
6. **Progress Tracking**
```
AI calls: compare_adr_progress
โ Monitors implementation against decisions
โ 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
---
## ๐ฏ Why MCP ADR Analysis Server is Powerful
### Traditional Approach (Without MCP)
```
You โ Generic AI โ 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 โ AI + MCP โ Specialized tools โ Your actual project โ 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
---
## ๐ 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:
```mermaid
graph LR
subgraph "Project Knowledge"
Tech[Technology Stack]
Arch[Architecture Patterns]
Decisions[ADR Decisions]
Issues[Identified Issues]
Progress[Implementation Progress]
end
Tech --> Decisions
Arch --> Decisions
Decisions --> Issues
Decisions --> Progress
Progress --> Tech
```
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
---
## ๐ 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 โ Analysis โ Decision โ Documentation โ Implementation โ 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
---
## ๐ฎ 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](./tutorials/01-first-steps.md)** - Hands-on introduction to using MCP
- **[API Reference](./reference/api-reference.md)** - Complete tool documentation
- **[Architecture Overview](architecture-decisions.md)** - Design decisions behind the server