Single tool 'create_ppt_from_text' has significant quality gaps. The tool name is acceptable (verb_noun format), but the description is vague (47 chars, below the 50-200 char optimal range for LLM optimization). The input schema contains only one parameter 'userText' with minimal constraint information, no enums, patterns, or length limits. The parameter description itself is generic and buried in a long comma-separated list of features rather than being explicit constraint documentation. No output schema is documented anywhere in the source code. Error handling exists at the implementation level (fetchAccountInfo, fetchYoutubeTranscript) but error messages are not designed to guide LLM recovery, they are operational errors, not user/agent-facing recovery guidance. The tool description does not clearly state WHAT the tool does (generate a presentation file? return a URL? modify state?), WHEN to use it, or WHAT is returned. The implementation shows the tool accepts a YouTube URL or text and optionally parses model/template/slide-count preferences from freeform text, this complex behavior is not reflected in the input schema, which shows only 'userText' as a free-form string. No schema validation, no enums for model or template choices, no explicit documentation of the parameter dependency pattern (how does parsing work? what values are recognized?). The tool definition violates the critical pattern check: parameters that accept one of a known set of values (model: gpt-4/gemini, template: ed-bullet-point1/ed-bullet-point2) must be declared as enums, but they are embedded in free-form text parsing logic instead.

• Convert 'userText' parameter to a structured schema with separate enum fields: 'content' (string, required), 'model' (enum: ['gpt-4', 'gemini'], optional), 'template' (enum: ['ed-bullet-point1', 'ed-bullet-point2'], optional), 'slide_count' (integer, 1-50, optional), 'include_images' (boolean, optional). Remove the free-form text parsing logic in favor of explicit schema validation.
• Document the output schema explicitly in the tool definition. The response should specify the structure: e.g., { 'presentation_url': string, 'slide_count': integer, 'generation_time_seconds': number } or similar. This is critical for the LLM to chain this tool with others.
• Expand the tool description to 100-150 characters and follow the WHAT/WHEN/RETURNS pattern: 'Generate a PowerPoint presentation from text content or a YouTube URL transcript. Returns a downloadable presentation URL. Use this when the user requests slides, a deck, or a presentation. Supports model and template selection.'
• Refactor parameter descriptions to be explicit constraints. Example: 'model: AI model for content generation (gpt-4 for advanced reasoning, gemini for speed). Default: gemini' and 'template: Slide layout style (ed-bullet-point1 for simple bullet lists, ed-bullet-point2 for detailed content). Default: ed-bullet-point1'.
• Add explicit error handling with recovery guidance. Example: If MAGICSLIDES_ACCESS_ID is missing, return { error: 'Server not configured. Contact the server administrator to set MAGICSLIDES_ACCESS_ID environment variable.' } instead of a generic runtime failure. If account plan is insufficient, return { error: 'user_plan_insufficient', message: 'Upgrade required', upgrade_url: '<URL>' } so the LLM can inform the user.