Server has 3 tools with significant quality gaps. learning_style_quiz_generator has a detailed description (good) but lacks visible input schema validation in the code, only Pydantic model definition exists without explicit schema registration to MCP. The two math tools (add, multiply) have minimal descriptions (8-18 chars, below 20-char threshold) and are defined in a test file, not the main server. No tool annotations, error handling guidance, or output schema documentation. Parameters lack depth beyond type hints.

• Expand descriptions for add and multiply to 50-100 characters explaining when to use them (e.g., 'Add two integers and return the sum. Use when the agent needs to perform arithmetic operations.').
• Explicitly serialize the LearningStyleQuizInput Pydantic model to JSON Schema and register it in the MCP tool definition. Verify the schema is visible in the tool's args_schema field.
• Document the output schema for learning_style_quiz_generator. Add a return type annotation to _run() and describe the structure: e.g., 'Returns a dict with keys: questions (list of Question objects), quiz_id (str).'
• Add try-catch and validation to learning_style_quiz_generator._run(). Return structured error responses like {'error': 'Invalid num_questions: must be 1-20', 'recovery': 'Try again with a valid count.'} instead of raising exceptions.
• Remove or integrate the math tools from test/server.py into the main server. If they are for testing only, document that they are not production tools. If they should be exposed, register them in main.py via a router.