Critical failures across all dimensions. The single tool 'execute_skill_action' has a vague, generic name that violates verb_noun conventions. Description is minimal (79 chars, below 100-char baseline for clarity). Input schema exists but lacks parameter descriptions entirely, both 'raw_sql' and 'execution_plan_json' have only type and description fields with no guidance on format, constraints, or valid ranges. No output schema is documented. The mcp_server.py implementation is a stub that prints JSON and exits; it does not actually implement MCP protocol message handling, tool invocation, or request/response cycles. Tool definitions appear inferred rather than explicitly registered via proper MCP mechanisms. No error handling, validation, or recovery guidance.

• Rename 'execute_skill_action' to 'analyze_query_plan' or 'recommend_indexes' to clearly signal the action and domain.
• Add detailed parameter descriptions: 'raw_sql' should specify format (valid SQL dialect, max length), and 'execution_plan_json' should document expected schema (e.g., PostgreSQL EXPLAIN JSON format).
• Document the output schema with all fields: query_hash, original_cost_units, optimized_cost_units, speedup_multiplier, tuning_recommendations (array of objects with recommendation_type, target_table, suggested_ddl, estimated_latency_reduction_pct, scanned_rows_reduction), index_impact_safe.
• Implement actual MCP protocol message handling in mcp_server.py: parse JSON-RPC requests, dispatch tool calls, validate inputs, and return structured responses with proper error handling.
• Add error handling: validate SQL syntax, check execution_plan_json structure, return actionable errors like 'Invalid SQL: syntax error at line 3' or 'Execution plan missing required fields: cost, rows_scanned'.