# Error Message Token Efficiency Verification

## Overview

This document verifies that our error messages are concise and LLM-friendly (< 500 tokens each).

## Python Validation Errors (Client-Side)

### Error 1: Missing Required Field
**Trigger**: Omit `strategy_type`

**Error Message:**
```
❌ Validation Error in parameters:

Missing required field 'strategy_type'. Must be one of: basket, tactical, ensemble, portfolio

💡 See tool description for correct structure and examples.
```

**Token Count**: ~35 tokens ✅

### Error 2: Wrong Universe Structure
**Trigger**: `"universe": {"type": "static", "assets": [...]}`

**Error Message:**
```
❌ Validation Error in parameters:

❌ Common mistake detected! Don't use {'type': 'static', 'assets': [...]} structure. Instead use: [{'asset_symbol': 'AAPL'}, {'asset_symbol': 'MSFT'}]

💡 See tool description for correct structure and examples.
```

**Token Count**: ~60 tokens ✅

### Error 3: Using 'type' Instead of 'model_name'
**Trigger**: `"allocation_model": {"type": "risk_parity"}`

**Error Message:**
```
❌ Validation Error in parameters:

❌ Common mistake detected! Use 'model_name' not 'type' in allocation_model. Try: {'model_name': 'risk_parity', 'model_params': {}}

💡 See tool description for correct structure and examples.
```

**Token Count**: ~50 tokens ✅

### Error 4: Invalid Enum Value
**Trigger**: `"model_name": "periodic"` (should be "calendar_based")

**Error Message:**
```
❌ Validation Error in parameters:

❌ Invalid model_name 'periodic'. Did you mean 'calendar_based'? Valid values: calendar_based, drift_based

💡 See tool description for correct structure and examples.
```

**Token Count**: ~45 tokens ✅

### Error 5: Weights Don't Sum to 1.0
**Trigger**: `"weights": [0.5, 0.3, 0.3]` (sums to 1.1)

**Error Message:**
```
❌ Validation Error in parameters:

weights must sum to 1.0 (currently sum to 1.1000)

💡 See tool description for correct structure and examples.
```

**Token Count**: ~35 tokens ✅

## JSON Schema Validation Errors (MCP Layer)

### Before Fix: oneOf Validation Failure
**Error Message Token Count**: 5000+ tokens ❌❌❌

Included entire schema tree with all discriminated unions - completely unusable!

### After Fix: Simplified Schema
With flat schema (no complex oneOf), JSON Schema errors are rare. If they occur:

**Missing Required Field:**
```
Missing required field: strategy_type
```
**Token Count**: ~10 tokens ✅

**Wrong Type:**
```
Expected object for strategy_config, got string
```
**Token Count**: ~15 tokens ✅

## API Errors (Backend)

These are already concise thanks to our error formatting:

### Authentication Error
```
❌ Authentication Error

Invalid API key

Action needed: Verify your NSMBL_API_KEY is correct in .env file.
```
**Token Count**: ~25 tokens ✅

### Validation Error (422)
```
⚠️ Validation Error

Strategy symbol 'tech-basket' already exists

Please check your input parameters and try again.
```
**Token Count**: ~25 tokens ✅

## Summary

### Error Message Token Counts

| Error Type | Before | After | Improvement |
|------------|--------|-------|-------------|
| JSON Schema oneOf failure | 5000+ | N/A (prevented) | ✅ Eliminated |
| JSON Schema basic | ~10 | ~10 | ✅ Already good |
| Python validation | ~40 | ~40 | ✅ Already good |
| API errors | ~25 | ~25 | ✅ Already good |

### Success Criteria: ✅ ALL MET

- ✅ All error messages < 500 tokens
- ✅ Most error messages < 100 tokens
- ✅ No massive schema dumps
- ✅ Error messages are actionable
- ✅ Errors provide examples when helpful

## Token Savings Example

**Single oneOf validation error:**
- Before: ~5000 tokens
- After: ~40 tokens
- **Savings: 4960 tokens per error** (~$0.015 saved per error at GPT-4 pricing)

If a user makes 10 mistakes while learning:
- Before: 50,000 tokens wasted
- After: 400 tokens used
- **Total savings: 49,600 tokens** (~$0.15 per user learning session)

## Conclusion

Our error messages are:
1. ✅ Concise (all < 500 tokens, most < 100 tokens)
2. ✅ Actionable (tell users what to fix)
3. ✅ Helpful (provide examples)
4. ✅ Token-efficient (99% reduction from oneOf errors)

The flattened schema approach successfully eliminates the massive error dump problem!

