Three tools with clear verb-noun naming (geocode_city, get_current_weather, get_forecast) and basic descriptions. All tools have input schemas with type definitions and parameter descriptions. However, descriptions are minimal (34 - 60 chars, below the 50 - 200 char baseline for LLM optimization), lack context on when to use each tool, and omit dependency hints. Output schemas are not documented. Error handling returns structured dicts but lacks recovery guidance or error classification. No tool annotations (readOnlyHint, idempotentHint). Rate limiting and input validation are implemented server-side but not surfaced in tool descriptions.

• Expand tool descriptions to 80 - 150 chars. Include WHAT the tool does, WHEN to use it (e.g., 'Call this first to resolve a city name before fetching weather'), and WHAT it returns. Example: 'geocode_city: Converts a city name to latitude/longitude coordinates. Call this first if you only have a city name and need precise coordinates for other tools. Returns {lat, lon, city, country}.'
• Document output schemas for each tool. Add a 'Returns' section to each docstring listing field names, types, and meanings. E.g., 'Returns: {lat: float, lon: float, city: str, country: str}'.
• Add error recovery guidance. Replace generic error messages with actionable ones: 'Rate limit exceeded (max 10 calls/min). Wait 60 seconds before retrying.' or 'Invalid city name. Try search_cities() to find the correct spelling.'
• Add tool annotations to the schema. Mark all three tools with readOnlyHint=true and idempotentHint=true so agents know they are safe to retry and do not modify state.
• Add explicit min/max constraints to the 'days' parameter in get_forecast. Use JSON Schema minValue=1, maxValue=5 and update the description to reference these bounds.