# Semantic Operation Refactor Summary
## Key Insights from WordPress MCP
The WordPress MCP demonstrates a paradigm shift from technical API wrappers to **semantic intent mapping**. Instead of exposing raw CRUD operations, it provides natural language-aligned operations that match how users think about their work.
### Core Innovation: Personality Modes
The "personality mode" system (borrowed from automotive ECU tuning) provides:
- **Role-appropriate tool exposure** - Users only see tools relevant to their role
- **Context-aware operations** - Same intent executes differently based on role
- **Progressive disclosure** - Complex features hidden until needed
- **Safety through constraints** - Dangerous operations restricted by role
## Value Proposition for Targetprocess MCP
### 1. Reduced Cognitive Load
**Current State**: AI must understand Targetprocess's complex data model
```typescript
// AI needs to know: Task vs UserStory, EntityState IDs, field names
await mcp.searchEntities({
type: "Task",
where: "AssignedUser.Id = 123 and EntityState.Id != 156",
include: ["Project", "Priority"]
});
```
**Semantic Approach**: Natural language operations
```typescript
// AI just expresses intent
await mcp.execute("show-my-tasks");
```
### 2. Role-Based Security at Semantic Level
**Current State**: All tools available to all users, security enforced by API
**Semantic Approach**: Tools filtered by personality before AI even sees them
### 3. Workflow Intelligence
**Current State**: AI must orchestrate multiple API calls
**Semantic Approach**: Single operation encapsulates entire workflow
```typescript
// "start-working-on" operation automatically:
// 1. Finds the task/story
// 2. Checks permissions
// 3. Updates state to "In Progress"
// 4. Assigns to current user
// 5. Logs time entry
// 6. Notifies team
// 7. Updates sprint burndown
```
## Implementation Architecture
### 1. Three-Layer Architecture
```
┌─────────────────────────────────────┐
│ Semantic Layer │ ← Natural language operations
├─────────────────────────────────────┤
│ Personality Layer │ ← Role-based filtering
├─────────────────────────────────────┤
│ API Layer │ ← Targetprocess API client
└─────────────────────────────────────┘
```
### 2. Feature Modules
Self-contained modules that encapsulate:
```typescript
interface FeatureModule {
metadata: {
id: string;
requiredPersonalities: string[];
};
operations: Record<string, SemanticOperation>;
}
```
### 3. Execution Context
Rich context passed to every operation:
```typescript
interface ExecutionContext {
user: { id, role, teams, permissions };
workspace: { currentProject, currentIteration };
personality: { mode, features, restrictions };
conversation: { mentionedEntities, intent };
}
```
## Practical Benefits
### For AI Agents
- **Simpler prompts** - "Show my tasks" vs complex API queries
- **Fewer errors** - Operations validated at semantic level
- **Better suggestions** - Context-aware next actions
### For Users
- **Natural interactions** - Speak in project management terms
- **Consistent behavior** - Same intent works across personalities
- **Safer operations** - Role-appropriate access control
### For Developers
- **Modular architecture** - Features can be added independently
- **Testable workflows** - Semantic operations are easily mocked
- **Clear boundaries** - Separation of concerns
## Migration Strategy
### Phase 1: Foundation (Current)
✅ Created personality configuration
✅ Defined semantic operation interface
✅ Built example operation (show-my-tasks)
✅ Documented architecture
### Phase 2: Core Operations (Next)
- [ ] Implement operation registry
- [ ] Build personality manager
- [ ] Create 5-10 core operations
- [ ] Add context management
### Phase 3: Integration
- [ ] Wire semantic layer to existing tools
- [ ] Add natural language intent mapping
- [ ] Implement workflow orchestration
- [ ] Create personality-specific tool filtering
### Phase 4: Intelligence
- [ ] Add operation suggestions
- [ ] Implement context inference
- [ ] Build workflow templates
- [ ] Add learning/adaptation
## Example: Complete User Journey
```typescript
// User: "I need to start the payment integration feature"
// 1. Personality Check
const personality = context.personality; // "developer"
// 2. Intent Recognition
const operation = registry.findByIntent("start feature"); // "start-feature-work"
// 3. Context Enhancement
const enrichedParams = {
featureName: "payment integration",
inferredProject: context.workspace.currentProject,
assignee: context.user.id
};
// 4. Semantic Execution
const result = await operation.execute(context, enrichedParams);
// This single operation:
// - Finds or creates the feature
// - Creates implementation tasks
// - Assigns to user
// - Updates sprint
// - Notifies team
// - Returns structured summary
// 5. Suggestions
// Based on result, suggest: "create-test-plan", "estimate-effort", "add-acceptance-criteria"
```
## Conclusion
The semantic operation approach transforms the Targetprocess MCP from a technical tool into an intelligent assistant that understands project management workflows. By mapping user intent to operations rather than exposing raw APIs, we create a more natural, secure, and powerful interface for AI-assisted project management.
This is not just a refactor - it's a fundamental shift in how we think about AI tool design.