process custom

The problem

Integrating bespoke logic or proprietary data processing into an AI agent often requires messy prompt engineering or fragile string parsing. You need a reliable way to trigger specific code paths and receive structured, predictable data.

What it does

• Executes the process_custom MCP tool to handle specialized data operations.
• Translates natural language intent into valid JSON tool arguments.
• Parses standardized response envelopes to distinguish between success and partial failure.
• Maps specific error codes like MISSING_PARAM or RATE_LIMITED to actionable user feedback.

Why this beats prompting it yourself

Generic prompts often hallucinate data structures or fail to handle API-level errors gracefully. This skill enforces a strict interface, ensuring your agent acts as a reliable bridge to your custom backend rather than guessing at the output format.

Use cases

• Triggering proprietary data processing pipelines via simple text commands.
• Standardizing JSON responses for downstream automation tasks.
• Handling structured error reporting for custom internal tools.

Known limitations

The tool has no persistent state between calls; all necessary context must be provided in every request.