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A Python-based autonomous task execution client that runs software tasks with self-healing error recovery capabilities
Single tool with critical definition gaps. Tool name 'run_autonomous_task' violates verb_noun clarity (generic 'run' instead of specific action). Description is 79 chars, below the 10-1024 baseline and lacks WHEN/WHY context. Input schema present but parameters lack depth: 'task_goal' description is generic; 'max_retries' lacks range constraints (e.g., 1-10). No output schema documented, LLM cannot predict response structure. No error handling guidance, no idempotency declaration, no security considerations. Tool appears to be a stub implementation (mock return values) rather than production-ready.
Executes an autonomous task with self-healing error recovery
Generic tool name 'run_autonomous_task' uses vague verb 'run' instead of specific action. 'run' is ambiguous, does it execute, schedule, simulate, or validate?
Tool description (79 chars) is below baseline minimum (10-1024 chars) and lacks critical context. Does not answer: What does it do? When to use it? What are prerequisites? LLM cannot determine when to select this tool over alternatives.
Parameter 'task_goal' description is generic ('The goal or objective...') and provides no format constraints, examples, or length limits. Parameter 'max_retries' lacks range specification (min/max bounds).
No output schema documented. Response structure (execution_result, self_healed_errors, completion_time_sec) is inferred from mock implementation only. LLM cannot plan downstream tool calls or extract fields reliably without explicit schema.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 27 | 2026-07-28+ | v2 |
No error handling guidance. Tool description does not explain failure modes, recovery steps, or whether operation is idempotent/retryable. Per pattern, error responses must tell LLM what to do next.