The server provides 5 tools with explicit schemas and descriptions. However, there are systematic issues: (1) Tool naming lacks action verbs, 'veo_generate_video', 'imagen_generate_image', 'gemini_generate_text', 'lyria_generate_music' all use 'generate' but are module-prefixed in a way that obscures the action. 'check_operation_status' is the only clearly verb-led tool. (2) Descriptions are present but generic (34 - 72 chars), below the baseline average of 194 chars. They state WHAT but not WHEN to use or key prerequisites. (3) Schemas are complete and well-structured with proper JSON Schema types, enums, and defaults, this is a strength. (4) Parameter descriptions exist but are minimal (8 - 30 chars) vs. baseline of 72 chars. (5) No output schemas documented, the code shows responses are wrapped in {content: [{type: 'text', text: JSON.stringify(...)}]} but the actual return structure is not specified. (6) Error handling is minimal, the code shows try/catch blocks that re-throw errors without actionable guidance. (7) Security: outputStorageUri is a parameter on multiple tools, which could allow exfiltration if not properly validated; no evidence of permission checks. Overall, this is a competent but minimal implementation, tools work, but descriptions and error handling fall short of production quality.

• Expand tool descriptions from ~40 chars to 100 - 200 chars. Include WHEN to use (e.g., 'Use for photorealistic product images, not diagrams' for imagen), and any limitations (max duration, model options, etc.). Reference the tool-description pattern baseline of 194 chars.
• Document output schemas explicitly. E.g., 'Returns {videoUrl: string, duration: number, createdAt: string, operationName?: string}'. This tells LLMs what fields to expect and what to pass to check_operation_status.
• Rename tools to follow verb_noun or resource_verb pattern. Consider: 'generate_video' (clearer order), 'video_generate' (resource first), or keep 'veo_generate_video' but add a description explaining that VEO 3 is Google's video model.
• Expand parameter descriptions to include constraints and examples. E.g., 'prompt (string, 10 - 1000 chars): Text description of the video. Be specific: "A red fox jumping over a fence in slow motion" works better than "fox jumping".'
• Add validation with actionable error messages. E.g., 'Invalid sampleCount: got 10, must be 1 - 4' instead of re-throwing the validation error without context.
• Implement permission checks for 'outputStorageUri'. Validate that the GCS bucket is in a whitelist or owned by the caller. Reject attempts to write to arbitrary buckets.
• If tools return operation IDs for async processing, clarify this in descriptions: 'Generates a video asynchronously. Returns an operationName to track progress with check_operation_status. Typical latency: 2 - 10 minutes.'