Single tool with minimal definition quality. Tool name 'execute_skill_action' is vague and generic (violates verb_noun clarity pattern). Description is 104 chars but lacks WHEN/WHY context for LLM selection. Input schema present but parameter 'query_payload' has only a 44-char description, insufficient for LLM reasoning. No output schema documented. No error handling guidance. No parameter constraints (enums, ranges, formats). Tool appears to be a catch-all wrapper rather than a focused, composable operation. Naming violates pattern:tool (should start with specific action verb like 'decompose_task' or 'execute_dag_step', not generic 'execute'). No evidence of idempotency, permission gates, or audit trails.

• Rename tool to a specific action verb: 'decompose_task_dag' or 'execute_dag_step'. This enables LLM to infer intent from the name alone.
• Expand tool description to 150-250 chars explaining: (1) What it does (decomposes tasks into DAG), (2) When to use it (when given a goal prompt), (3) What it returns (plan_id, topological_order, execution_dag). Reference pattern:tool-description.
• Document input schema: specify that 'query_payload' must be a JSON string containing {goal_prompt, max_parallel_workers} or reference a formal schema. Add constraints: goal_prompt length 10-2000 chars, max_parallel_workers 1-16.
• Document output schema explicitly in tool definition. Specify fields: plan_id (string), topological_order (array of strings), execution_dag (array of objects with step_id, title, deps), critical_path_latency_sec (number), concurrency_factor (number).
• Add error handling: return structured errors with actionable guidance. E.g., 'Invalid goal_prompt: must be 10-2000 characters. Received: <length>. Please provide a more detailed task description.' Reference pattern:recovery-guide.