PanchayatAI exposes 5 tools with minimal schema definition and poor naming conventions. Tool names lack action verbs (sarpanch_agent, learning_agent, etc. are nouns, not verbs like 'get_', 'create_', 'generate_'). Input schemas are present but extremely sparse: most tools accept only 1-2 string parameters with minimal type information. Descriptions exist but are verbose (200+ chars) and lack actionable guidance on WHEN to use each tool or what it returns. No output schemas are documented. No error handling guidance. Tools appear to be agent orchestration wrappers rather than discrete, composable operations. The codebase shows LLM invocations with prompt injection risks (user input directly interpolated into prompts) and no input validation.

• Rename tools to start with action verbs: 'generate_career_roadmap', 'generate_learning_plan', 'generate_productivity_schedule', 'generate_health_guidance', 'identify_risks'. This enables LLMs to infer intent from the name alone.
• Add enum constraints to input parameters. E.g., 'career_goal' could accept predefined categories (software_engineer, data_scientist, product_manager) or require 10-200 character descriptions with format validation.
• Document output schemas explicitly. E.g., learning_agent returns {learning_roadmap: {phase_1: [string], phase_2: [string], ...}, skills_order: [string], recommended_courses: [string], ...}. Make this a formal JSON Schema.
• Sanitize user input before interpolating into prompts. Use parameterized prompt templates or escape special characters. Validate input length and content type.
• Add explicit error handling: catch JSON parsing failures, return structured error responses with recovery hints (e.g., 'Knowledge base returned insufficient context. Try a more specific career goal.').