generate data

The problem

Standard LLM outputs are often unstructured, inconsistent, and prone to hallucinations when generating specific datasets. Developers need a reliable way to transform text into validated JSON without managing complex regex or manual parsing.

What it does

• Calls the generate_data MCP tool to process textual input into structured results.
• Handles error states gracefully with standardized codes like MISSING_PARAM and RATE_LIMITED.
• Enforces a consistent JSON envelope for every response, ensuring predictable data handling.
• Supports custom configuration via an optional options object for fine-tuned data generation.

Why this beats prompting it yourself

Hand-written prompts often fail to maintain strict JSON schemas or handle upstream errors. This skill provides a formalized interface and error-handling logic that prevents the model from "making up" data when a tool call fails.

Use cases

• Transforming raw unstructured text into valid JSON objects for database ingestion.
• Generating synthetic datasets based on specific textual descriptions.
• Building automated pipelines that require a "skill personalizado a medida" trigger.
• Standardizing responses from external tools into a unified ok/data/error format.

Known limitations

The tool is stateless and does not remember context between calls. It requires all necessary data to be passed in every invocation.