get data

The problem

Integrating custom data sources into AI workflows often leads to inconsistent response formats or unhandled exceptions that break the agent loop. Manual handling of API errors and data extraction lacks the necessary structure for reliable automation.

What it does

• Interfaces with the get_data MCP tool to execute custom data retrieval operations.
• Standardizes outputs into a predictable JSON envelope with explicit success and error states.
• Handles common failure modes like rate limits and missing parameters without crashing the agent.
• Translates raw tool responses into concise, human-readable summaries.

Why this beats prompting it yourself

This skill enforces a strict execution protocol that prevents the model from inventing data when tools fail. It provides built-in logic for error code interpretation, ensuring the agent reacts correctly to upstream issues rather than retrying blindly or hallucinating results.

Use cases

• Executing custom data extraction tasks via structured input.
• Processing textual data through standardized offline operations.
• Debugging data retrieval flows using specific error code mapping.

Known limitations

The tool maintains no state between calls, requiring full context in every invocation. It is designed for single-shot operations and does not support automated looping.