Two tools are explicitly defined with basic MCP registration via @mcp.tool() decorators in weather.py. Both tools have simple descriptions and input schemas, but the schemas lack critical detail, descriptions are minimal, and there is no output schema documentation. Error handling is present but returns unstructured strings rather than actionable error messages. No parameter validation, no pagination support, no security patterns applied. The server is functionally minimal and would not pass code review by a production team.
Output schema not documented. Tools return free-form strings rather than structured JSON objects. LLMs cannot reliably parse responses or extract chaining IDs for downstream calls.
Input schema lacks detail. 'state' parameter has no constraints (enum, pattern, length limit). 'latitude' and 'longitude' lack range constraints (e.g. -90 to 90). LLMs cannot validate inputs and may pass invalid coordinates (e.g. latitude=500).
Error messages are unstructured and non-actionable. 'Unable to fetch alerts or no alerts found' does not tell the LLM whether to retry, suggest an alternative tool, or ask the user for clarification. Errors lack recovery guidance.
No parameter validation. The 'state' parameter accepts any string; the function does not validate that it is exactly 2 uppercase letters. Invalid input will silently fail at the API layer with a cryptic error.
Recommendations
Add structured output schema documentation for both tools. Define the response shape (e.g. {alerts: [{event, area, severity, description, instructions}]} for get_alerts; {periods: [{name, temperature, wind, forecast}]} for get_forecast). This enables LLMs to plan downstream operations.
Add constraints to input parameters. For get_alerts, define state as an enum of valid US state codes (CA, NY, TX, etc.) or a regex pattern (^[A-Z]{2}$). For get_forecast, add range constraints: latitude [-90, 90], longitude [-180, 180].
Replace generic error messages with actionable guidance. Instead of 'Unable to fetch alerts', return: 'No alerts found for state XX. Possible causes: invalid state code (must be 2-letter US state), or NWS API unreachable. Try a different state or check your connection.'
Validate state parameter before making the API call. Return a clear error: 'Invalid state code: got "CA1". Must be 2 uppercase letters (e.g. CA, NY, TX).'
Expand tool descriptions. Get_alerts: 'Fetch active weather alerts issued by the National Weather Service for a US state. Call this when the user asks about hazards, warnings, or emergency conditions in a location. Returns a list of alerts with severity, event type, and instructions.' (120-150 chars, actionable context). Get_forecast: 'Fetch a 7-day weather forecast for a specific latitude/longitude. Call this when the user asks about upcoming weather, temperature, wind, or conditions for a location. Returns periods with temperature, wind direction/speed, and detailed forecast text.' (similar length and structure).
Tool descriptions are generic and lack context. 'Get weather alerts for a US state' does not explain when to use this vs get_forecast, what the return format is, or what error conditions are possible. Descriptions should be 50-200 chars with examples of when to call.
No pagination or result limits. get_forecast arbitrarily returns only 5 periods (hardcoded slice), and get_alerts returns all features without pagination. Large result sets will bloat context and degrade LLM reasoning.
Descriptions mention example values ('e.g. CA, NY') that LLMs may reuse literally instead of adapting to context. Should use an enum constraint instead.
get_alerts
Add pagination or result limiting. For get_alerts, accept optional parameters: limit (default 20, max 100), offset (default 0). Document: 'Returns up to [limit] alerts. Use offset to page through results.' For get_forecast, document why only 5 periods are returned and offer a period_count parameter (default 5, max 14).
Remove example values from descriptions (e.g. 'e.g. CA, NY'). State instead: 'Two-letter US state code (enum from US state abbreviations).' Pair with a proper enum schema field.
Add per-tool error classification. Both tools should document: Retryable errors (timeout, transient API failure) vs. user-fixable errors (invalid state code, coordinates out of range) vs. fatal (NWS service permanently unavailable). Pair with actionable next steps.
Consider adding a dependency note if tools chain: E.g., 'If you have an address, call address_to_coordinates() first to get latitude/longitude, then pass them to this tool.' This guides multi-step planning and prevents wasted calls.
Return structured JSON objects, not formatted strings. Instead of 'Event: Tornado Warning\nArea: ...' as a single string, return {event: 'Tornado Warning', area: '...', severity: '...', ...}. Structured output lets LLMs reliably extract fields and chain results.