Four tools with mixed quality. activate_skill and skill_read_reference have reasonable descriptions (60-80 chars) and proper schemas with typed parameters. greet and mcp_ping have minimal descriptions (under 40 chars) and lack context. No tool descriptions explain when to use them, what they return, or dependencies. Parameter descriptions are sparse. No output schemas documented. Error handling absent. Tool names lack clear action verbs (activate_skill is borderline; skill_read_reference is compound). No evidence of pagination, batching, or idempotent operation patterns.

• Expand tool descriptions to 50-200 characters. For greet: 'Generates a personalized greeting message for a given name. Use this to create friendly, context-aware responses. Returns a greeting string.' For mcp_ping: 'Echoes back a ping message to test MCP server connectivity. Use for health checks or latency measurement. Returns the original message with a timestamp.'
• Document output schemas for all tools. Example for greet: {"type": "object", "properties": {"greeting": {"type": "string", "description": "The greeting message"}, "timestamp": {"type": "string", "format": "date-time"}}, "required": ["greeting"]}
• Add error handling guidance to descriptions. E.g., activate_skill: 'If skill not found, returns available skill names. If skill activation fails, returns error reason and suggests alternative skills.'
• Enhance parameter descriptions with constraints and examples. For activate_skill name param: 'The name of the skill to activate (case-sensitive, alphanumeric + underscore). Call list_skills first to discover available options. Example: "python_executor"'
• Consider splitting skill_read_reference into read_skill_reference (requires pre-activated skill) and list_skill_files (discovers available files in a skill) to reduce coupling and improve composability.