Three tools with basic schemas and descriptions, but significant quality gaps. Tool names lack action verbs (generate_Lessonplan, generate_flashcards, Topicwise_MCQ_Generator use inconsistent casing). Descriptions are present but generic, they lack WHEN to use guidance, prerequisites, or recovery hints. Parameters have types and basic descriptions, but lack constraints (enums, ranges, format specs). Output is unstructured text (StringIO), not typed objects, LLMs cannot parse results reliably. No error handling, no pagination support, no idempotency guarantees. Naming inconsistency (snake_case vs PascalCase) signals low polish. Per-tool average: 42.

• Rename tools to verb_noun format with consistent snake_case: generate_lesson_plan, generate_flashcards, generate_mcq_questions. Verify names start with action verbs (get, create, generate, search, etc.).
• Return structured JSON objects instead of formatted text strings. Define output schemas in tool docstrings: @mcp.tool() def generate_lesson_plan(...) -> dict with keys: title, subject, learning_objectives, main_topics (array of {title, subtopics, ...}), etc.
• Add input validation and error handling. Wrap Groq API calls in try-except; validate topic (non-empty, <200 chars), MCQ_number (1-50); return structured errors: {error: 'Invalid MCQ_number', details: 'Must be 1-50, got 0', recovery: 'Try again with a valid number'}.
• Add parameter constraints to descriptions. lesson_topic: 'Topic name (1-200 chars, e.g. "Photosynthesis")'. MCQ_number: 'Number of questions to generate (1-50, default 5)'. extra_information: 'Optional custom instructions (max 500 chars)'.
• Enhance tool descriptions with WHEN-to-use and prerequisites. E.g., 'Generate a structured lesson plan with learning objectives, main topics, subtopics, activities, and assessments. Use this to create comprehensive teaching materials. Requires GROQ_API_KEY environment variable.'