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dhis2_android_generate_data_models

Generate Android data model classes and repositories for DHIS2 entities using specified architecture patterns and data binding approaches.

Instructions

Generate Android data model classes and repositories for DHIS2 entities

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entitiesYesDHIS2 entities to generate models for
architectureYesArchitecture pattern for data layer
dataBindingNoDatabase binding approach
validationNo
serializationNoJSON serialization library

Implementation Reference

  • MCP tool handler that calls generateDataModelsConfiguration with input arguments and returns markdown output.
    case 'dhis2_android_generate_data_models':
      const dataModelsArgs = args as any;
      const dataModelsConfig = generateDataModelsConfiguration(dataModelsArgs);
      return {
        content: [
          {
            type: 'text',
            text: dataModelsConfig,
          },
        ],
      };
  • Helper function that generates the markdown configuration for Android data models based on input arguments (entities and architecture). This is the core logic executed by the handler.
    export function generateDataModelsConfiguration(args: any): string {
      return `# DHIS2 Android Data Models Configuration
    
    Entities: ${args.entities.join(', ')}
    Architecture: ${args.architecture}
    
    ## Implementation details for data models...
    `;
    }
  • Tool name registered in the permission system mapping, requiring 'canUseMobileFeatures' permission.
    ['dhis2_android_generate_data_models', 'canUseMobileFeatures'],
  • Import statement bringing the helper function into scope for the handler.
      generateDataModelsConfiguration,
      generateAndroidTestingConfiguration,
      generateAndroidUIConfiguration,
      generateOfflineAnalyticsConfiguration,
      generateNotificationsConfiguration,
      generatePerformanceOptimizationConfiguration
    } from './android-generators.js';
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't cover critical traits such as whether it modifies existing files, requires specific permissions, outputs file locations, or handles errors. For a code generation tool with multiple parameters, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded and wastes no space, making it highly concise and well-structured for quick understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (5 parameters, nested objects, no output schema, and no annotations), the description is incomplete. It doesn't explain what the output looks like (e.g., generated files, structure), how to handle the generated code, or any prerequisites. For a tool that likely produces significant code artifacts, this lack of context is a notable gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 80%, providing good documentation for parameters like 'entities' and 'architecture'. The description adds no additional parameter semantics beyond what's in the schema, but since the schema coverage is high, the baseline score of 3 is appropriate as the schema handles most of the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Generate') and target resources ('Android data model classes and repositories for DHIS2 entities'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'dhis2_generate_ui_data_display' or 'dhis2_generate_test_setup', which also involve generation but for different components, so it misses full sibling distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. For example, it doesn't mention if this is for initial project setup, adding new features, or how it relates to siblings like 'dhis2_android_init_project' or 'dhis2_generate_app_runtime_config'. The description lacks context for decision-making.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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