# MCP Server Purpose & Workflow Testing Plan
## 🎯 Purpose of the MCP ADR Analysis Server
### Primary Purpose
The MCP ADR Analysis Server is an **AI-powered architectural analysis platform** that enhances AI coding assistants (Claude, Cursor, Cline) with deep architectural decision-making capabilities. It provides **actual analysis results**, not just prompts.
### Core Functions
1. **Architectural Analysis** 🏗️
- Analyze project technology stacks
- Detect architectural patterns
- Identify implicit decisions
- Link code to architectural decisions
2. **ADR Management** 📋
- Generate ADRs from requirements (PRD → ADRs)
- Discover existing ADRs
- Suggest missing ADRs
- Validate ADR compliance
3. **Decision Tracking** 🔗
- Maintain knowledge graph of decisions
- Track implementation progress
- Link code files to decisions
- Validate code against architectural rules
4. **Security & Compliance** 🛡️
- Detect sensitive content
- Mask sensitive information
- Security audit capabilities
5. **Workflow Orchestration** 🔄
- Intelligent tool sequencing
- Workflow guidance
- Multi-tool coordination
### Target Users
- **AI Coding Assistants** - Claude, Cursor, Cline, Windsurf
- **Enterprise Architects** - Documenting architectural decisions
- **Development Teams** - Tracking implementation progress
### Key Differentiator
Unlike generic AI assistants, this server:
- ✅ Accesses **actual project files**
- ✅ Returns **real analysis results** (not prompts)
- ✅ Maintains **knowledge graph** of decisions
- ✅ Provides **actionable insights** with confidence scoring
## 🧪 Workflow Testing Plan
### Why Test Workflows?
Workflows test **end-to-end scenarios** that users actually perform:
- Not just individual tools, but **complete sequences**
- Tests **tool coordination** and **data flow**
- Validates **real-world usage patterns**
### Test Scenarios to Validate
#### Scenario 1: New Project Analysis Workflow
**Purpose**: Test complete project discovery and ADR generation
**Workflow Steps**:
1. `analyze_project_ecosystem` → Understand tech stack
2. `discover_existing_adrs` → Find any existing ADRs
3. `suggest_adrs` → Identify missing decisions
4. `get_architectural_context` → Get comprehensive context
5. `generate_adr_from_decision` → Create ADR for key decision
**Expected Outcome**:
- Complete project understanding
- Identified architectural decisions
- Generated ADR document
#### Scenario 2: PRD to Implementation Workflow
**Purpose**: Test requirements-to-ADR-to-todo pipeline
**Workflow Steps**:
1. `generate_adrs_from_prd` → Convert PRD to ADRs
2. `generate_adr_todo` → Extract implementation tasks
3. `smart_score` → Evaluate project health
4. `validate_rules` → Check compliance
**Expected Outcome**:
- ADRs generated from PRD
- TODO.md with implementation tasks
- Health score and compliance status
#### Scenario 3: Security Audit Workflow
**Purpose**: Test security analysis capabilities
**Workflow Steps**:
1. `analyze_content_security` → Scan for sensitive data
2. `generate_content_masking` → Generate masking rules
3. `validate_content_masking` → Verify masking effectiveness
**Expected Outcome**:
- Security issues identified
- Masking configuration generated
- Validation results
#### Scenario 4: Workflow Guidance Workflow
**Purpose**: Test AI-powered workflow recommendations
**Workflow Steps**:
1. `get_workflow_guidance` → Get recommended workflow
2. `tool_chain_orchestrator` → Generate execution plan
3. Execute recommended tools in sequence
**Expected Outcome**:
- Intelligent workflow recommendations
- Structured tool execution plan
- Successful workflow completion
### Sample Repository Structure
We'll use a **small, focused sample project** that includes:
```
sample-project/
├── package.json # Node.js project with dependencies
├── server.js # Express API server
├── README.md # Project documentation
├── docs/
│ └── adrs/
│ ├── 001-database-architecture.md
│ ├── 002-api-authentication.md
│ └── 003-legacy-data-migration.md
└── .env.example # Environment configuration
```
**Why Small?**
- ✅ Fast test execution
- ✅ Easy to understand results
- ✅ Clear validation of workflow steps
- ✅ Representative of real projects
### What We're Testing
1. **Tool Sequencing** - Do tools work together correctly?
2. **Data Flow** - Does output from one tool feed into the next?
3. **Connection Reuse** - Does connection pooling work across workflow?
4. **Error Handling** - How does the workflow handle failures?
5. **Real-World Patterns** - Do common workflows actually work?
### Success Criteria
✅ **All workflow steps complete successfully**
✅ **Data flows correctly between tools**
✅ **No connection errors** (thanks to connection reuse)
✅ **Generated artifacts are valid** (ADRs, TODOs, etc.)
✅ **Workflow provides actionable insights**
## 📊 Expected Test Results
### Test Coverage
- ✅ Individual tool tests (already passing)
- ✅ Connection reuse (already fixed)
- ⏳ **Workflow end-to-end tests** (what we're adding)
### Validation Points
- Each workflow step succeeds
- Tools receive correct input from previous steps
- Generated files are valid and complete
- Workflow produces expected outcomes
## 🎯 Testing Approach
1. **Use Small Sample Repo** - Fast, focused testing
2. **Test Real Workflows** - Actual user scenarios
3. **Validate Outputs** - Check generated files
4. **Test Tool Chains** - Multi-step sequences
5. **Verify Integration** - Tools work together
This validates that the server works not just for individual tools, but for **complete architectural analysis workflows** that users actually perform.