Two tools with reasonable naming but weak descriptions and incomplete schema documentation. Tool names follow verb_noun pattern (store_image_metadata, generate_image), but descriptions lack depth on when/why to use each tool. Parameter descriptions are present but sparse. No output schema documentation. Error handling is minimal with generic exception messages. The server demonstrates basic structure but falls short of production quality, descriptions average ~90 chars (below 194 baseline), parameter docs are minimal, and no guidance on error recovery or tool composition.

• Expand tool descriptions to 50 - 150 chars each. For store_image_metadata: 'Store the generated image's prompt, URL, and embedding vector in the database for later retrieval and search. Call this after generate_image returns a queued status and the image is available.'
• Document generate_image output as: {status: string, image_id: string?, request_id: string?} so callers know what to expect and can chain to store_image_metadata.
• Add explicit enums and constraints to parameter descriptions. For model_type, specify 'One of: stable-diffusion (fastest), openai-dalle (highest quality), mock (testing only).'
• Document the dto_data parameter structure. Either provide a schema inline ('For recipe model: {title: string, ingredients: array of strings}') or reference separate parameter docs.
• Enhance error messages with recovery hints. Instead of 'Unsupported type', return 'Unsupported type "xyz". Try one of: stable-diffusion, openai-dalle, mock.'
• Move database credentials from hardcoded values to environment variables. Use os.getenv('DB_HOST', 'db') and validate at startup.
• Add a tool composition note in the docstring: 'Typical flow: call generate_image → wait for image availability → call store_image_metadata with the returned image_id and embedding.'
• Document expected embedding dimensions. The code expects 768-dimensional float arrays, state this in the embedding parameter description.