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Tools
| Name | Description |
|---|---|
dynamodb_data_model_validation | Validates and tests DynamoDB data models against DynamoDB Local. Use this tool to validate, test, and verify your DynamoDB data model after completing the design phase. This tool automatically checks that all access patterns work correctly by executing them against a local DynamoDB instance. WHEN TO USE: - After completing data model design with dynamodb_data_modeling tool - When user asks to "validate", "test", "check", or "verify" their DynamoDB data model - To ensure all access patterns execute correctly before deploying to production WHAT IT DOES: 1. If dynamodb_data_model.json doesn't exist: - Returns complete JSON generation guide from json_generation_guide.md - Follow the guide to create the JSON file with tables, items, and access_patterns - Call this tool again after creating the JSON to validate 2. If dynamodb_data_model.json exists: - Validates the JSON structure (checks for required keys: tables, items, access_patterns) - Sets up DynamoDB Local environment (Docker/Podman/Finch/nerdctl or Java fallback) - Cleans up existing tables from previous validation runs - Creates tables and inserts test data from your model specification - Tests all defined access patterns by executing their AWS CLI implementations - Saves detailed validation results to dynamodb_model_validation.json - Transforms results to markdown format for comprehensive review WHAT TO DO ON SUCCESSFUL COMPLETION: After validation completes, you MUST present the user with TWO options: 1. Deploy to AWS: Call `generate_resources` tool with resource_type='cdk' to create a CDK app for provisioning tables 2. Generate Python code: Call `dynamodb_data_model_schema_converter` to convert the model to schema.json, then generate code The user can choose one or both options. If they choose CDK first, you can still generate Python code afterward. |
dynamodb_data_modeling | Retrieves the complete DynamoDB Data Modeling Expert prompt. This tool returns a prompt to help user with data modeling on DynamoDB. The prompt guides through requirements gathering, access pattern analysis, and schema design. The prompt contains: - Structured 2-phase workflow (requirements ā final design) - Enterprise design patterns: hot partition analysis, write sharding, sparse GSIs, and more - Cost optimization strategies and RPS-based capacity planning - Multi-table design philosophy with advanced denormalization patterns - Integration guidance for OpenSearch, Lambda, and analytics Usage: Simply call this tool to get the expert prompt. Returns: Complete expert system prompt as text (no parameters required) |
generate_data_access_layer | Generate Python code for a data access layer to interact with your DynamoDB tables. š“ PREREQUISITE: Before calling this tool, you MUST first call `dynamodb_data_model_schema_converter` to generate schema.json from dynamodb_data_model.md. This tool ONLY accepts schema.json. TYPICAL WORKFLOW: 1. Complete data modeling with `dynamodb_data_modeling` tool (creates dynamodb_data_model.md) 2. Validate with `dynamodb_data_model_validation` tool (optional but recommended) 3. Optionally deploy infrastructure with `generate_resources` tool (resource_type='cdk') 4. Convert to schema: Call `dynamodb_data_model_schema_converter` tool (creates schema.json) 5. Generate code: Call this `generate_data_access_layer` tool with the path to schema.json This tool generates a complete data access layer from your schema including: - Type-safe entity classes with field validation using Pydantic - Repository classes with optimistic locking and error handling for all operations - Fully implemented access patterns - Working usage examples with realistic sample data (if usage_data_path provided) |
source_db_analyzer | Analyzes source database to extract schema and access patterns for DynamoDB modeling. WHEN TO USE: Call this tool when the user selects "Existing Database Analysis" option after invoking the `dynamodb_data_modeling` tool. This extracts schema and query patterns from an existing relational database to accelerate DynamoDB data model design. IMPORTANT: Always ask the user which execution mode they prefer before calling this tool. Execution Modes: - self_service: Generates SQL queries for user to run manually, then parses their results. - managed (MySQL only): Database connection via RDS Data API or hostname. Supported Databases: MySQL, PostgreSQL, SQL Server, Oracle Output: Generates analysis files (schema structure, access patterns, relationships) in Markdown format. These files feed into the DynamoDB data modeling workflow to inform table design, GSI selection, and access pattern mapping. Returns: Analysis summary with file locations and next steps. |