Loading ToolBench
A tool-using agent for deep-space photonics thermal mitigation advisory. Combines a physics-based thermal drift simulator, an XGBoost strategy classifier, and a scenario knowledge store to recommend thermal management strategies for space instruments.
Three tools with moderate definition quality. Tool names follow verb-noun convention (simulate_, classify_, search_) and are action-oriented, which is good. However, descriptions lack depth and parameter annotations are minimal. Input schemas are present and properly structured as JSON Schema, but lack critical metadata like enums for constrained inputs, minimum/maximum bounds for numeric fields, and detailed guidance on expected formats. None of the tools include output schema documentation, agents cannot predict what fields to expect. The tools appear to be READ_ONLY (no state modification), which is good for safety, but this is not declared in tool annotations. Error handling is minimal; the code shows basic None checks but no actionable error messages that guide LLM recovery.
Predict the recommended mitigation strategy (Passive, Active, or Hybrid) with calibrated probabilities using the trained XGBoost model.
Retrieve the most similar prior thermal scenarios and their proven mitigation strategies from the scenario data store.
Compute the refractive index shift, mechanical strain, and risk level for a chip material in a deep-space environment using first-principles physics.
No output schemas documented for any tool. LLMs cannot plan downstream tool calls or extract required fields without knowing what the response structure contains.
Numeric and enum constraints missing from parameter definitions. 'delta_t' has no min/max bounds; 'material' and 'environment' have no enum values or validation guidance. Free-form strings invite LLM hallucination.
Error handling is minimal. Code checks for None (classifier, datastore) and returns {'error': '...'} strings, but no guidance on what the LLM should do next (retry, ask user, abort).
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 47 | <=2025-11-25 | v2 |
No tool annotations (readOnlyHint, destructiveHint) declared. All three tools are read-only and safe, but LLMs cannot infer this.
No parameter descriptions for 'material' in classify_strategy. Field names like 'thermal_effect' lack guidance on expected format (free-form text, enum, structured JSON?).
search_thermal_knowledge: 'top_k' parameter lacks documented bounds. Code defaults to 3, but no min/max specified. LLMs could pass arbitrary large values.