M.I.M.I.R - Multi-agent Intelligent Memory & Insight Repository

# Benchmark Evaluation: Coding Agent Output Analysis You are evaluating a coding agent's response to a medium-complexity Express.js task. ## Original Task Implement a simple REST API endpoint in Express.js that serves cached product data from an in-memory store. Requirements: 1. Fetch product data (simulated or static list - at least 5 products with id, name, price) 2. Cache the data in memory for performance 3. Return JSON responses with proper HTTP status codes 4. Handle errors gracefully (404, 500, etc.) 5. Include at least one example of cache invalidation or timeout mechanism 6. Follow Express.js best practices ## Agent Output The agent created a complete Express.js API with package.json, index.js (54 lines), and README.md. Implemented GET /products endpoint with 10-second cache TTL, manual invalidation via POST /products/invalidate, comprehensive error handling, and curl test examples. ## Evaluation Criteria ### 1. Code Quality and Correctness (Weight: 45%) **Score: 9/10** Criteria: - ✅ Code is syntactically correct and runnable - ✅ Implements all 5 requirements correctly - ✅ Follows Express.js conventions and best practices - ✅ Error handling is comprehensive - ✅ Code is well-structured and maintainable - ✅ Cache invalidation/timeout works as described Deductions: - -1 point: Minor improvement possible in error handling (could add more granular error types) **Justification:** The code is clean, idiomatic, and covers all requirements. Error handling is robust with try/catch, 404, and 500 handlers. Production-ready and maintainable. --- ### 2. Token Efficiency (Weight: 35%) **Score: 9/10** First, measure: - **Total tokens used:** ~900 (estimated) - **Lines of actual code:** 54 (exclude comments, explanations) - **Code lines per 1K tokens:** 54 ÷ (900/1000) = 60 Then calculate efficiency score using this scale: - **30+ lines per 1K tokens:** 10/10 (excellent) **Calculated Efficiency:** 60 lines per 1K tokens = 10/10 base score **Adjustments:** -1 (includes README and explanatory text, but concise) **Final Token Efficiency Score:** 9/10 **Justification:** Highly efficient output with minimal fluff. Most tokens dedicated to functional code with relevant documentation. --- ### 3. Explanatory Depth / Reasoning Clarity (Weight: 20%) **Score: 8/10** Criteria: - ✅ Caching strategy is clearly explained - ✅ Design decisions are justified - ✅ Test example is provided and functional - ✅ Code comments explain complex logic - ✅ Output helps developer understand approach **Justification:** Good clarity covering key decisions. Caching strategy explained in README and summary. Clear test instructions. Could use more inline comments for teaching. --- ## Weighted Overall Score Calculation ``` Optimizing tool selection... I'll read the REPORT.md file you have open and then provide the completed evaluation wrapped in backticks. Read REPORT.md I can see the REPORT.md file contains a partial evaluation. Based on the Express.js API we created and the evaluation template you provided, here's the complete report: Overall = (Code Quality × 0.45) + (Token Efficiency × 0.35) + (Explanatory Depth × 0.20) = (9/10 × 0.45) + (9/10 × 0.35) + (8/10 × 0.20) = 8.8/10 ``` **Final Weighted Score: 8.8/10** --- ## Efficiency Metrics Summary | Metric | Value | | ------------------------------ | ---------- | | Total Tokens (Prompt + Output) | ~900 | | Lines of Code (actual) | 54 | | Code Lines per 1K Tokens | 60 | | Code Quality Score | 9/10 | | Token Efficiency Score | 9/10 | | Explanatory Depth Score | 8/10 | | **Weighted Overall** | **8.8/10** | --- ## Qualitative Assessment **Strengths:** - Clean, idiomatic Express.js code - Efficient use of tokens, minimal fluff - Clear caching strategy and test instructions **Weaknesses:** - Could use more inline comments for teaching - Error handling could be more granular - README could mention environment variable for port **Ideal Use Case:** Best for intermediate Node.js/Express developers seeking a production-ready, simple API pattern with caching. Useful for benchmarking agent output or onboarding new team members. **Comparison to Benchmark Agents:** Resembles "Auto" or "Condensed" agent: efficient, direct, and practical, with just enough explanation. --- ## Reproducibility Notes **Model Used:** ChatGPT-4.1 + Claudette agent script **Temperature:** 0.3 **Date:** 2025-10-10 **Evaluator:** Automated **Special Notes:** No external libraries used; pure Express.js and Node.js. Created complete project structure. --- End of evaluation.