Single tool with minimal definition quality. Tool name 'execute_skill_action' is generic and does not follow verb_noun pattern clearly (execute_skill_action vs. execute_task or schedule_dag). Description is marketing-focused ('Multi-Agent Consensus & Swarm Orchestration') rather than functional. Input schema exists but parameter 'query_payload' is a single string with no format guidance, type constraints, or examples. No output schema documented, response is hardcoded JSON string without field definitions. No error handling, validation, or recovery guidance. Tool appears to be a stub that always returns success regardless of input.

• Rename tool to 'schedule_task_dag' or 'execute_dag_workflow' to clearly indicate it schedules/executes a directed acyclic graph of tasks. Follow verb_noun pattern.
• Replace 'query_payload' string parameter with structured input: define expected fields (workflow_id, tasks, workers_count, etc.) with types, descriptions, and constraints. Reference the client.py schedule_task_dag() signature.
• Document output schema with fields: schedule_id (string), workflow_id (string), total_tasks (integer), total_execution_waves (integer), cyclic_deadlock_detected (boolean), estimated_makespan_sec (integer), execution_waves (array of arrays), dag_visualization_url (string). Include descriptions for each field.
• Rewrite description: 'Schedule a hierarchical task DAG for parallel execution. Accepts workflow definition, detects cyclic deadlocks, and returns execution plan with estimated makespan. Use this to decompose multi-step tasks into parallelizable waves.'
• Implement actual error handling: validate input payload structure, return error if workflow_id is missing or invalid, return error if task count exceeds limits, provide actionable error messages (e.g., 'Cyclic dependency detected in tasks T2→T4→T2. Reorder tasks to break cycle.').