# WordPress MCP Architecture Analysis
## Overview
The WordPress MCP server demonstrates a sophisticated approach to building Model Context Protocol servers that goes beyond simple API wrappers. It introduces several architectural patterns that can significantly improve the Targetprocess MCP implementation.
## Key Architectural Principles
### 1. Semantic Operation Mapping
Instead of directly exposing API endpoints, WordPress MCP creates high-level semantic operations that represent user intentions:
- **Traditional approach**: `createPost()`, `updatePost()`, `setPostStatus()`
- **Semantic approach**: `publish-workflow`, `submit-for-review`, `draft-article`
Each semantic operation encapsulates:
- Business logic and workflows
- Permission checks
- State transitions
- Error handling
- Context-aware execution
### 2. Personality-Based Tool Filtering
The system implements a "personality mode" system inspired by automotive ECU mappings:
```
Contributor Mode (Economy) → Limited, safe operations
Author Mode (Comfort) → Balanced content management
Administrator Mode (Performance) → Full system control
```
Benefits:
- Reduces cognitive load on AI agents
- Prevents overwhelming users with inappropriate tools
- Ensures role-appropriate interactions
- Maintains security through least-privilege access
### 3. Feature Registry System
Features are self-contained modules with:
- Descriptive metadata
- Input validation schemas
- Eligibility checks
- Execution logic
Example structure:
```javascript
{
name: 'create-article',
description: 'Create an article with publish options',
personalities: ['author', 'editor', 'administrator'],
schema: { /* JSON Schema */ },
execute: async (context, input) => { /* logic */ }
}
```
### 4. Context-Aware Execution
The system maintains execution context that includes:
- Current user personality
- WordPress connection state
- Session information
- Feature eligibility
This allows features to adapt their behavior based on runtime conditions.
## Architecture Components
### Core Components
1. **Feature Discovery** (`feature-discovery.js`)
- Dynamically loads features from filesystem
- Validates feature structure
- Registers with feature registry
2. **Feature Mapper** (`feature-mapper.js`)
- Maps features to personalities
- Handles runtime filtering
- Manages feature availability
3. **Personality Manager** (`personality-manager.js`)
- Loads personality configurations
- Validates feature assignments
- Provides personality-based filtering
4. **Scope Rule Engine** (`scope-rule-engine.js`)
- Implements fine-grained access control
- Categories: content, action, resource scopes
- Dynamic rule evaluation
5. **Tool Injector** (`tool-injector.js`)
- Dynamically injects tools based on personality
- Handles tool lifecycle
- Manages tool availability
### Feature Organization
Features are organized into logical groups:
- **Content**: Article creation, editing, publishing
- **Management**: User management, site configuration
- **Media**: File uploads, media library management
Each feature is a self-contained module that can be:
- Independently developed
- Easily tested
- Dynamically loaded
- Conditionally exposed
## Value Propositions
### 1. Reduced Complexity
- AI agents work with high-level concepts, not low-level APIs
- Business logic is encapsulated in features
- Error handling is centralized
### 2. Improved Security
- Role-based access is enforced at multiple levels
- Features can have dynamic eligibility checks
- Least-privilege principle is built-in
### 3. Better User Experience
- Tools are contextually relevant
- Workflows match user mental models
- Operations are semantic, not technical
### 4. Maintainability
- Features are modular and isolated
- New features can be added without affecting existing ones
- Configuration-driven behavior
## Lessons for Targetprocess MCP
### 1. Implement Semantic Operations
Transform current direct API mappings into semantic operations:
Current:
```
- search_entities
- create_entity
- update_entity
```
Proposed semantic operations:
```
- plan-sprint
- assign-to-team
- track-progress
- review-backlog
- close-iteration
```
### 2. Create Role-Based Personalities
Define Targetprocess-specific personalities:
```
Developer Mode: Focus on tasks, bugs, code integration
Scrum Master Mode: Sprint planning, team velocity, ceremonies
Product Owner Mode: Backlog management, prioritization, roadmaps
Stakeholder Mode: Read-only dashboards, reports, metrics
```
### 3. Build a Feature System
Structure features as self-contained modules:
```typescript
interface Feature {
name: string;
description: string;
category: 'planning' | 'tracking' | 'reporting' | 'administration';
personalities: PersonalityType[];
schema: ZodSchema;
execute: (context: Context, input: any) => Promise<Result>;
isEligible?: (context: Context) => boolean;
}
```
### 4. Implement Context Management
Maintain rich context throughout operations:
```typescript
interface Context {
personality: PersonalityType;
project?: Project;
team?: Team;
iteration?: Iteration;
user: User;
permissions: Permission[];
}
```
### 5. Create Workflow Orchestration
Build higher-level workflows that combine multiple operations:
- Sprint planning workflow
- Bug triage workflow
- Release management workflow
- Team onboarding workflow
## Implementation Priorities
1. **Phase 1**: Create feature registry and loader system
2. **Phase 2**: Define personalities and mapping system
3. **Phase 3**: Refactor existing tools into semantic features
4. **Phase 4**: Implement context management
5. **Phase 5**: Build workflow orchestration
## Conclusion
The WordPress MCP architecture demonstrates that MCP servers can be much more than API wrappers. By implementing semantic operations, personality-based filtering, and intelligent abstractions, we can create a Targetprocess MCP that:
- Is easier for AI agents to use effectively
- Provides role-appropriate tool exposure
- Encapsulates business logic and workflows
- Maintains security through proper access control
- Offers a superior user experience
The key insight is to think in terms of user intentions and workflows rather than API endpoints, creating a semantic layer that makes complex systems accessible and manageable through natural language interactions.