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8c10cf2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | # SQLAssistant-MCP Evaluation Scripts
Evaluation tools for analyzing SQLAssistant-MCP multi-agent system execution results across different LLM models.
## Scripts
### analyze_retry_patterns.py
Analyzes RETRY patterns in execution traces, including:
- Orchestrator-level retries (exception handling)
- Business logic retries (compliance check failures)
**Output:** `retry_summary.csv`, `retry_analysis.json`, `error_by_agent.csv`, `business_retry_chapters.csv`
### evaluate_scores.py
Collects score statistics from `execution_log.json` files.
**Output:** `score_summary.csv`, `score_analysis.json`
### evaluate_success.py
Measures success rate based on whether `get_database_schema` tool was called (indicates proper database exploration vs hallucination).
**Output:** `success_rate.csv`, `success_detailed_results.json`
### evaluate_trajectory-Filter_Tools.py
Evaluates trajectory metrics against reference trajectory:
- Exact match
- In-order match
- Any-order match
- Precision / Recall
- Single-tool use
- Path diversity metrics
**Output:** `evaluation_results.csv`, `any_order_match_failures.csv`
## Configuration
`reference_trajectory.yaml` defines:
- Reference trajectory (ground truth)
- Target tools for evaluation
- Models to evaluate
- Extraction types (SPAN, Chain, Agent, LLM, Tool)
## Usage
```bash
python analyze_retry_patterns.py
python evaluate_scores.py
python evaluate_success.py
python evaluate_trajectory-Filter_Tools.py --config reference_trajectory.yaml
```
## Requirements
- Python 3.8+
- pandas
- pyyaml
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