| # SQLAssistant-MCP Evaluation Scripts |
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| Evaluation tools for analyzing SQLAssistant-MCP multi-agent system execution results across different LLM models. |
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| ## Scripts |
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| ### analyze_retry_patterns.py |
| Analyzes RETRY patterns in execution traces, including: |
| - Orchestrator-level retries (exception handling) |
| - Business logic retries (compliance check failures) |
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| **Output:** `retry_summary.csv`, `retry_analysis.json`, `error_by_agent.csv`, `business_retry_chapters.csv` |
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| ### evaluate_scores.py |
| Collects score statistics from `execution_log.json` files. |
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| **Output:** `score_summary.csv`, `score_analysis.json` |
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| ### 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` |
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|
| ### 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 |
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| **Output:** `evaluation_results.csv`, `any_order_match_failures.csv` |
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| ## Configuration |
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| `reference_trajectory.yaml` defines: |
| - Reference trajectory (ground truth) |
| - Target tools for evaluation |
| - Models to evaluate |
| - Extraction types (SPAN, Chain, Agent, LLM, Tool) |
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|
| ## Usage |
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|
| ```bash |
| python analyze_retry_patterns.py |
| python evaluate_scores.py |
| python evaluate_success.py |
| python evaluate_trajectory-Filter_Tools.py --config reference_trajectory.yaml |
| ``` |
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|
| ## Requirements |
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|
| - Python 3.8+ |
| - pandas |
| - pyyaml |
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