This repository is a collection of Spring AI MCP client/server EXAMPLES, not a production MCP server implementation. The codebase provides Spring Boot starter configurations and client demos but does NOT contain actual MCP tool definitions with schemas, descriptions, or proper registration. The single tool 'weatherTools' is inferred from a reference to 'MethodToolCallbackProvider from WeatherService class' in McpServerApplication.java, but no explicit tool definition, schema, input parameters, descriptions, or output documentation are visible in the provided source. The pom.xml files show dependency on 'spring-ai-mcp-server-sse-demo' and 'spring-ai-openai-spring-boot-starter', but the actual tool implementation code is not included in the code sample provided. Without seeing the WeatherService class implementation, parameter definitions, return types, or error handling, scoring is limited to what can be verified: NONE of the required tool definition elements (schema, descriptions, parameter types) are visible. Per the hard scoring rule: 'If you cannot see the actual tool definition in the source (only inferred): cap that tool's overall at 50.' In this case, the tool is ONLY inferred by name from a comment, so the cap is 50; but since no schema or description is visible, both those dimensions score 0.

• Create explicit tool registration with full JSON Schema. Example: { 'name': 'get_weather', 'description': 'Retrieve current weather conditions and forecast for a specified location. Returns temperature, precipitation, wind, and forecast for the next 5 days.', 'inputSchema': { 'type': 'object', 'properties': { 'location': { 'type': 'string', 'description': 'City name or coordinates (e.g., "San Francisco" or "37.7749,-122.4194")' }, 'units': { 'type': 'string', 'enum': ['celsius', 'fahrenheit'], 'description': 'Temperature unit for the response' } }, 'required': ['location'] } }
• Rename 'weatherTools' to a clear action verb like 'get_weather', 'fetch_forecast', or 'search_weather_by_location'. Action verbs help LLMs parse intent from the name alone.
• Add comprehensive descriptions for each tool (50-200 characters): state WHAT it does, WHEN to use it (vs. similar tools), and what it returns. Example: 'Get current weather and 5-day forecast for a location. Use this to answer questions about weather conditions, temperature trends, and precipitation chances.'
• Document the return schema with all fields: temperature, unit, precipitation %, wind speed, forecast_days, etc. Include field types and descriptions so the LLM knows what to extract.
• Add error handling guidance: document what happens when location is not found, API is down, or coordinates are invalid. Provide recovery hints (e.g., 'If location not found, try with a larger region name' or 'Retry after 30 seconds if timeout').
• If weatherTools is actually a group of separate tools (e.g., get_current_weather, get_forecast, get_alerts), split them into individual registered tools with separate schemas and descriptions.