Feature Request: Add tool success/failure metrics for production monitoring

Expected Behavior

Spring AI should provide success/failure metrics for individual tool calls to enable production monitoring and reliability tracking.

Proposed Metrics

1. Tool Result Counter: spring.ai.tool.result

• Tags: tool.name, success (true/false)
• Tracks individual tool execution outcomes

2. Tool Error Counter: spring.ai.tool.error

• Tags: tool.name, error.type
• Tracks tool failures by error type

Code Example

# Configuration (optional, enabled by default)
spring:
 ai:
   tool:
     metrics:
       enabled: true
       include-error-details: false 


# View tool success metrics
curl http://localhost:8080/actuator/metrics/spring.ai.tool.result

# Expected response:
{
  "name": "spring.ai.tool.result",
  "measurements": [
    { "statistic": "COUNT", "value": 45 }
  ],
  "availableTags": [
    { "tag": "tool.name", "values": ["getCurrentWeather", "getRecommendation"] },
    { "tag": "success", "values": ["true", "false"] }
  ]
}

# Filter by specific tool and outcome
curl "http://localhost:8080/actuator/metrics/spring.ai.tool.result?tag=tool.name:getCurrentWeather&tag=success:false" 

Implementation Approach

Add a new ToolMetricsObservationHandler that extends the existing observation system:

@Component
@ConditionalOnClass({ObservationRegistry.class, MeterRegistry.class})
public class ToolMetricsObservationHandler implements ObservationHandler<Observation.Context> {
    
    @Override
    public void onStop(Observation.Context context) {
        if (context instanceof ToolCallingObservationContext toolContext) {
            String toolName = toolContext.getToolDefinition().name();
            boolean isSuccess = !toolContext.hasError();
            
            Counter.builder("spring.ai.tool.result")
                .tag("tool.name", toolName)
                .tag("success", String.valueOf(isSuccess))
                .register(meterRegistry)
                .increment();
        }
    }
}

Current Behavior

Spring AI currently provides basic spring.ai.tool timer metrics through the existing observation system:

✅ Tool execution time (COUNT, TOTAL_TIME, MAX)
✅ Basic tool call tracking
❌ Success/failure breakdown
❌ Error rate monitoring
❌ Tool reliability insights

The existing metrics are generated automatically by Micrometer's DefaultMeterObservationHandler, but lack domain-specific insights needed for production monitoring.

Context

How has this issue affected you?
When running AI applications in production, it's critical to monitor tool reliability. Currently, there's no way to:

Identify which tools are failing frequently
Set up alerts for tool failure rates
Track tool performance degradation over time
Monitor SLA compliance for tool-dependent services

Start by reading Spring AI’s existing tool observation system, including ToolCallingObservationContext and the DefaultMeterObservationHandler path, to determine where stopped tool outcomes and the MeterRegistry are available. Done means production monitoring exposes the proposed result and error metrics with the specified tags, configuration behavior, and actuator responses.