VoltSentry defines a single tool 'analyze_grid_stress' with a clear, domain-specific purpose. The tool name follows verb_noun convention (analyze_grid_stress). The description is comprehensive (280+ chars) and explains what the tool does, when to use it, and what it returns. The input schema uses Zod with an enum constraint for the region parameter, which is well-described. However, the tool lacks output schema documentation, the response structure is not formally defined in the tool registration, forcing LLMs to infer the output format from the description alone. Error handling is minimal; there's no guidance on what happens if the grid data fetch fails or if a region is temporarily unavailable. The tool is read-only (no state modification), which is appropriate for grid monitoring.

• Add a formal output schema to the tool registration. Define the response structure with typed fields: { stress_score: number (0-100), status_label: enum, demand_gap_mw: number, generation_headroom_pct: number, nuclear_outage_status: string, temperature_correlation: string, nerc_context: string, verdict: string }. This enables LLMs to reliably extract and chain on specific fields.
• Enhance error handling with actionable recovery guidance. Return structured errors: { error: string, code: 'api_unavailable'|'region_unsupported'|'data_stale', retry_after_seconds?: number, fallback_regions?: string[] }. Guide LLMs on whether to retry, try a neighboring region, or ask the user.
• Expand the region parameter description to explain coverage, data freshness SLA, and what happens if real-time data is unavailable. Example: 'US grid balancing authority region code (NERC). Data updated every 5 minutes. If real-time data unavailable, returns last cached report (up to 1 hour old).'
• Document the response structure in the tool description. Mention that the verdict is plain-English and actionable, and that the stress score is calibrated against NERC thresholds. This helps LLMs understand what to do with the output.
• Add a 'confidence' or 'data_freshness' field to the response to signal when the report is based on real-time vs. cached data. This helps agents decide whether to retry or escalate.