Loading ToolBench
Three read-only tools with basic descriptions and minimal input schemas. Tool names follow verb_noun pattern (prometheus_query, active_alerts, target_health) but descriptions are terse (18-48 chars, well below the 50-200 char baseline for LLM optimization). prometheus_query has a single string parameter with validation rules embedded in description; active_alerts and target_health have no parameters. No output schemas documented. Input validation occurs at runtime (validate_query), not declared in schema. Audit logging present but not exposed to LLM. No error recovery guidance in descriptions.
Retrieve currently active alerts from Prometheus
Execute an instant query against Prometheus
Get health status summary of Prometheus scrape targets
Tool descriptions are too brief (18-48 chars vs. 50-200 char baseline). LLMs cannot determine when to select these tools or what they return. 'Execute an instant query against Prometheus' lacks context on output structure, when to use vs. active_alerts, and error cases.
No output schemas documented. LLMs cannot plan downstream operations or extract fields. prometheus_query returns a Prometheus API response dict, but structure is opaque to the agent.
prometheus_query parameter description embeds validation rules ('1-1000 non-NUL characters') as text rather than JSON Schema constraints (minLength, maxLength, pattern). LLMs cannot parse these rules reliably.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 52 | 2026-07-28+ | v2 |
No error recovery guidance. If prometheus_query fails (invalid PromQL, timeout, Prometheus unreachable), descriptions do not tell the LLM what to do next or how to self-correct.
target_health truncates results silently (max_items config, returns truncated flag) but no pagination parameters (limit, offset) or next_cursor. Agents cannot iterate over large target lists.