Server has 4 tools with basic schemas and descriptions, but significant gaps limit production readiness. All tools have descriptions (10-100 chars, mostly adequate) and input schemas with Zod validation. However, output schemas are undocumented, responses return raw text or JSON without field-level documentation. Parameter descriptions exist but lack detail on constraints, formats, and error recovery. No tool annotations (readOnlyHint/destructiveHint). Error handling is minimal: failures return generic text without actionable guidance. Tool naming follows verb_noun convention (get_, search_, make_) but lacks clarity on side effects. The make-kaggle-notebook-with-dataset tool modifies state but has no confirmation or dry-run pattern.

• Document output schemas for all tools. Specify field names, types, and meanings. E.g., search-kaggle-datasets should document: returns array of {id, title, owner, description, size_bytes, download_count}.
• Add tool annotations: mark get-* and search-* tools with readOnlyHint=true; mark make-kaggle-notebook-with-dataset with destructiveHint=true.
• Enhance error messages with recovery guidance. Replace 'Failed to retrieve Croissant for X' with 'Dataset not found. Verify the dataset handle is correct (format: owner/dataset). Try search-kaggle-datasets to find the dataset.'
• Add parameter constraints to descriptions. E.g., searchQuery: 'Search term (1-200 chars, alphanumeric + spaces). Example: "climate data 2024"'. notebookContent: 'Python code as a string. Will be wrapped in a Jupyter notebook cell.'
• Implement confirmation pattern for make-kaggle-notebook-with-dataset. Add optional dry_run parameter (default false) that returns the notebook metadata without creating it. Or require explicit confirmation via a separate confirm_notebook_creation tool.