# Dynamic Registry Enhancement Suggestions for AI Agents

> [!WARNING]
> Historical/internal brainstorming note. Content is not a guaranteed description of currently shipped APIs.
> Use root docs and `docs/` as the source of truth.

## Current State Analysis

The dynamic registry feature provides excellent foundations for AI agent workflows but could be enhanced in several key areas:

## 1. Enhanced Tool Discovery and Metadata

### Current Limitation

`fmt_list_client_tools_and_resources` provides basic tool definitions but lacks runtime context.

### Suggested Enhancement

Add metadata about tool state, usage frequency, and categorization:

```dart
// Enhanced tool registration with metadata
final debugToolEntry = MCPCallEntry.tool(
  handler: debugHandler,
  definition: MCPToolDefinition(
    name: 'inspect_widget_state',
    description: 'Inspect current widget state',
    inputSchema: {...},
    metadata: {
      'category': 'debugging',
      'priority': 'high',
      'usageHint': 'Use when UI behaves unexpectedly',
      'estimatedExecutionTime': 'fast',
      'sideEffects': false,
    },
  ),
);
```

### Implementation

Extend `MCPToolDefinition` and `DynamicRegistryTools._handleListClientToolsAndResources` to include:

- Tool categories (debugging, state, ui, performance)
- Execution time estimates
- Side effect indicators
- Usage recommendations
- Last execution timestamp

## 2. Intelligent Tool Suggestion System

### Current Limitation

AI agents must manually discover which tools are relevant for specific scenarios.

### Suggested Enhancement

Add contextual tool recommendations based on app state:

```dart
// New tool for intelligent suggestions
static final suggestRelevantTools = Tool(
  name: 'suggestRelevantTools',
  description: 'Get tool suggestions based on current app state and context',
  inputSchema: ObjectSchema(
    properties: {
      'context': Schema.string(description: 'Current debugging context (error, performance, ui)'),
      'appState': Schema.object(description: 'Current app state snapshot'),
    },
  ),
);
```

### Implementation

- Analyze current Flutter app state (errors, performance metrics, UI tree)
- Return prioritized list of relevant tools
- Include reasoning for each suggestion

## 3. Tool Chain Execution

### Current Limitation

AI agents must execute tools sequentially, making debugging workflows verbose.

### Suggested Enhancement

Enable chained tool execution with dependency management:

```dart
// New tool for executing tool chains
static final executeToolChain = Tool(
  name: 'executeToolChain',
  description: 'Execute a sequence of tools with result passing',
  inputSchema: ObjectSchema(
    required: ['toolChain'],
    properties: {
      'toolChain': Schema.array(
        description: 'Array of tools to execute in sequence',
        items: Schema.object(
          properties: {
            'toolName': Schema.string(description: 'Tool to execute'),
            'arguments': Schema.object(description: 'Tool arguments'),
            'resultMapping': Schema.object(description: 'How to pass results to next tool'),
          },
        ),
      ),
    },
  ),
);
```

### Example Usage

```json
{
  "name": "executeToolChain",
  "arguments": {
    "toolChain": [
      { "toolName": "get_widget_info", "arguments": { "widgetId": "button1" } },
      {
        "toolName": "modify_widget_state",
        "arguments": { "widgetId": "button1", "enabled": false }
      },
      { "toolName": "take_screenshot", "arguments": {} },
      { "toolName": "hot_reload_flutter", "arguments": {} }
    ]
  }
}
```

## 4. Real-time State Monitoring

### Current Limitation

AI agents get point-in-time snapshots but lack continuous monitoring.

### Suggested Enhancement

Add subscription-based monitoring for continuous state updates:

```dart
// New resource for real-time monitoring
static final subscribeToStateChanges = Tool(
  name: 'subscribeToStateChanges',
  description: 'Subscribe to real-time app state changes',
  inputSchema: ObjectSchema(
    properties: {
      'monitors': Schema.array(
        description: 'Types of changes to monitor',
        items: Schema.string(enum: ['errors', 'ui_changes', 'navigation', 'performance']),
      ),
      'duration': Schema.integer(description: 'Monitoring duration in seconds'),
    },
  ),
);
```

### Implementation

- Use Flutter's dev tools protocol for real-time updates
- Stream changes to AI agents via MCP notifications
- Enable proactive debugging based on detected issues

## 5. Code Generation and Hot Injection

### Current Limitation

AI agents can only work with pre-registered tools, limiting experimentation.

### Suggested Enhancement

Enable dynamic Dart code generation and injection:

```dart
// New tool for runtime code injection
static final injectDartCode = Tool(
  name: 'injectDartCode',
  description: 'Inject and execute Dart code at runtime for experimentation',
  inputSchema: ObjectSchema(
    required: ['dartCode'],
    properties: {
      'dartCode': Schema.string(description: 'Dart code to inject and execute'),
      'context': Schema.string(description: 'Execution context (widget, service, global)'),
      'temporary': Schema.bool(description: 'Whether changes are temporary or persistent'),
    },
  ),
);
```

### Safety Considerations

- Sandbox execution environment
- Code validation before injection
- Automatic rollback on errors
- Clear temporary vs persistent change distinction

## 6. Enhanced Error Context

### Current Limitation

Error reporting lacks sufficient context for AI agents to provide meaningful assistance.

### Suggested Enhancement

Enrich error reporting with actionable context:

```dart
// Enhanced error resource
extension type EnhancedErrorResource._(MCPCallEntry entry) implements MCPCallEntry {
  factory EnhancedErrorResource() {
    return EnhancedErrorResource._(MCPCallEntry.resource(
      handler: (request) => MCPCallResult(
        message: 'Enhanced error information with context',
        parameters: {
          'errors': errors.map((e) => {
            'message': e.message,
            'stackTrace': e.stackTrace,
            'widgetContext': e.widgetContext,
            'suggestedFixes': e.suggestedFixes,
            'relatedCode': e.relatedCodeSnippets,
            'debuggingSteps': e.recommendedDebuggingSteps,
          }).toList(),
        },
      ),
      definition: MCPResourceDefinition(
        name: 'enhanced_errors',
        description: 'Detailed error information with actionable context',
      ),
    ));
  }
}
```

## 7. Performance Monitoring Integration

### Current Limitation

No performance monitoring tools for AI agents to detect and debug performance issues.

### Suggested Enhancement

Add comprehensive performance monitoring:

```dart
// Performance monitoring tools
static final analyzePerformance = Tool(
  name: 'analyzePerformance',
  description: 'Analyze app performance and identify bottlenecks',
  inputSchema: ObjectSchema(
    properties: {
      'duration': Schema.integer(description: 'Analysis duration in seconds'),
      'metrics': Schema.array(
        description: 'Performance metrics to collect',
        items: Schema.string(enum: ['frameRate', 'memory', 'cpu', 'battery', 'networkUsage']),
      ),
    },
  ),
);
```

## 8. Tool Template System

### Current Limitation

Creating new tools requires significant boilerplate code.

### Suggested Enhancement

Provide tool templates for common debugging patterns:

```dart
// Tool template system
static final createToolFromTemplate = Tool(
  name: 'createToolFromTemplate',
  description: 'Create a new debugging tool from a template',
  inputSchema: ObjectSchema(
    required: ['templateType'],
    properties: {
      'templateType': Schema.string(
        enum: ['state_inspector', 'ui_modifier', 'performance_probe', 'custom_assertion'],
      ),
      'parameters': Schema.object(description: 'Template-specific parameters'),
    },
  ),
);
```

## Implementation Priority

### High Priority (Immediate Impact)

1. Enhanced tool discovery metadata
2. Tool chain execution
3. Enhanced error context

### Medium Priority (Workflow Improvement)

4. Intelligent tool suggestions
5. Performance monitoring integration
6. Tool template system

### Lower Priority (Advanced Features)

7. Real-time state monitoring
8. Code generation and hot injection

## Review Questions

1. **Scope**: Are these enhancements aligned with your vision for AI agent workflows?

2. **Implementation**: Which enhancements would provide the most immediate value for AI agents?

3. **Safety**: Are there additional safety considerations for features like code injection?

4. **Integration**: How should these enhancements integrate with existing Flutter development tools?

5. **Documentation**: What additional documentation or examples would help AI agents use these features effectively?
