# MCP Protocol Research
**Status**: Complete | **Date**: 2025-12-10
## Core Definition
MCP = "USB-C port for AI applications"
- Open-source standard connecting AI apps to external systems
- Standardized connectivity for data sources, services, workflows
## Server Architecture
**Three Feature Categories**:
1. **Resources**: Static/dynamic data sources clients can read
- File contents, database records, API responses
- Support both static and streaming patterns
2. **Tools**: Functions servers implement for client invocation
- Defined input schemas (JSON Schema)
- Execution logic with error handling
- Return structured results
3. **Prompts**: Pre-built instruction templates
- Guide AI model behavior for specific tasks
- Parameterizable with context injection
**Request-Response Model**:
- Clients initiate communication
- Servers respond with results or errors
- Synchronous execution pattern
## Transport Mechanisms
**Stdio**: Local process communication
- Standard input/output streams
- Suitable for CLI tools, local agents
**SSE (Server-Sent Events)**: Remote HTTP streaming
- HTTP-based for network connections
- Real-time updates support
**Transport Abstraction**: Same server logic across different transports
## Tool Definition Structure
```typescript
{
name: string,
description: string,
inputSchema: JSONSchema,
handler: async (params) => result
}
```
## Initialization Sequence
1. **Capability Negotiation**:
- Server declares supported features
- Client indicates requirements
- Establish shared understanding
2. **Operational Communication**:
- Client requests tools/resources
- Server executes and returns results
## Security Considerations
- Authorization frameworks for sensitive resources
- Proper credential handling (never log/expose)
- Validate all client requests
- Principle of least privilege for resource access
## Best Practices
**Tool Design**:
- Clear, specific schemas
- Descriptive names and documentation
- Proper error handling and logging
**Server Implementation**:
- Version your implementation
- Document resource formats explicitly
- Consider scalability in resource delivery
- Handle concurrent requests gracefully
**Error Handling**:
- Structured error responses
- Clear error messages for clients
- Log errors with context for debugging
## Integration with Cloudron
**Architecture Decision**:
- Use **stdio transport** for simplicity (local SuperClaude integration)
- Implement **tools category** (Cloudron API operations)
- Defer resources/prompts until needed
**Tool Mapping Strategy**:
1. App Management tools: install, restart, backup, configure
2. Domain tools: add, verify, sync DNS
3. User tools: create, permissions, groups
4. System tools: status, logs, monitoring
**Error Strategy**:
- Wrap Cloudron API errors in MCP error format
- Add context (endpoint called, parameters)
- Return actionable error messages
**Security**:
- API token via environment variable
- Validate token before server starts
- Never log token or sensitive data
- Implement rate limiting wrapper if needed
**Next Steps**:
- Design tool schemas for top 10 Cloudron operations
- Implement MCP server with stdio transport
- Create comprehensive error handling
- Test with SuperClaude framework integration
