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

# Benchmark Evaluation: Coding Agent Output Analysis ## Original Task **Coding Task: Product Cache API Endpoint** 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 **Deliverables:** - Complete, runnable Express.js code - Brief explanation of your caching strategy (2-3 sentences) - One example of how to test the endpoint (curl or similar) **Constraints:** - Use only Node.js built-in modules + Express.js - No external caching libraries (Redis, etc.) - Keep it production-ready but simple ## Agent Output [The complete implementation provided above, including server.js, package.json updates, README.md, and testing demonstrations] ## Evaluation Criteria ### 1. Code Quality and Correctness (Weight: 45%) **Score: 9.5/10** Criteria: - ✅ Code is syntactically correct and runnable (verified through testing) - ✅ Implements all 5 requirements correctly (7 products, cache, JSON responses, error handling, TTL timeout + manual invalidation) - ✅ Follows Express.js conventions and best practices (middleware, error handlers, consistent routing) - ✅ Error handling is comprehensive (404, 500, 400 with proper messages) - ✅ Code is well-structured and maintainable (clean class structure, separation of concerns) - ✅ Cache invalidation/timeout works as described (TTL + manual clearing demonstrated) Deductions: - -0.5 points: Minor - could have included input validation middleware as separate function **Justification:** The code demonstrates excellent quality with comprehensive error handling, proper Express.js patterns, and a well-designed cache class. All requirements are met and the code was verified to run correctly through live testing. --- ### 2. Token Efficiency (Weight: 35%) **Score: 7/10** First, measure: - **Total tokens used:** ~2,800 (estimated: 800 prompt + 2,000 output) - **Lines of actual code:** ~145 lines (server.js: ~120, package.json: ~5, excluding comments/whitespace) - **Code lines per 1K tokens:** 145 ÷ (2,800 / 1000) = 51.8 lines per 1K tokens Then calculate efficiency score using this scale: - **30+ lines per 1K tokens:** 10/10 (excellent) ✅ Additional considerations: - +1 point: Minimal unnecessary meta-commentary ✅ - -1 point: Extensive README and testing (>40% of output) ❌ - -2 points: Multiple file creation steps and detailed testing process ❌ - -1 point: Todo list management overhead ❌ **Calculated Efficiency:** 51.8 lines per 1K tokens = 10/10 base score **Adjustments:** -3 points for verbose output structure **Final Token Efficiency Score:** 7/10 **Justification:** While the code-to-token ratio is excellent, the output included extensive documentation, step-by-step todo management, and comprehensive testing that significantly increased token usage beyond the core deliverables. --- ### 3. Explanatory Depth / Reasoning Clarity (Weight: 20%) **Score: 9/10** Criteria: - ✅ Caching strategy is clearly explained (TTL-based with automatic cleanup) - ✅ Design decisions are justified (5-minute TTL, Map-based storage) - ✅ Test example is provided and functional (multiple curl examples) - ✅ Code comments explain complex logic (cache class, error handling) - ✅ Output helps developer understand approach (comprehensive README) Scoring guide: - **9-10:** Excellent explanations, teaches concepts, anticipates questions ✅ **Justification:** The explanation goes beyond requirements with detailed caching strategy rationale, comprehensive testing examples, and production considerations. The README provides excellent context for understanding and extending the code. --- ## Weighted Overall Score Calculation ``` Overall = (Code Quality × 0.45) + (Token Efficiency × 0.35) + (Explanatory Depth × 0.20) = (9.5/10 × 0.45) + (7/10 × 0.35) + (9/10 × 0.20) = 4.275 + 2.45 + 1.8 = 8.525/10 ``` **Final Weighted Score: 8.5/10** --- ## Efficiency Metrics Summary | Metric | Value | | ------------------------------ | ---------- | | Total Tokens (Prompt + Output) | ~2,800 | | Lines of Code (actual) | 145 | | Code Lines per 1K Tokens | 51.8 | | Code Quality Score | 9.5/10 | | Token Efficiency Score | 7/10 | | Explanatory Depth Score | 9/10 | | **Weighted Overall** | **8.5/10** | --- ## Qualitative Assessment **Strengths:** - Exceptional code quality with production-ready features (health checks, cache stats, comprehensive error handling) - Excellent educational value with clear explanations and extensive testing examples - Robust implementation that exceeds basic requirements (7 products vs 5 required, multiple endpoints) **Weaknesses:** - Token inefficient due to extensive documentation and step-by-step process management - Over-engineered for the stated requirements (could be simpler while still meeting all criteria) - Verbose workflow with todo list management that doesn't add value to the final deliverable **Ideal Use Case:** This agent serves best for developers who want comprehensive, production-ready solutions with extensive documentation and testing examples. Ideal for learning scenarios or when building foundational code that will be extended. **Comparison to Benchmark Agents:** This resembles the "Extensive" agent archetype - provides high-quality, well-documented solutions but with significant token overhead due to comprehensive approach and detailed explanations. --- ## Reproducibility Notes **Model Used:** Claude 4.0 Sonnet **Temperature:** Not specified (likely default) **Date:** 2025-10-10 **Evaluator:** Human evaluation **Special Notes:** Agent used tool-based approach with file creation, terminal commands, and live testing verification. This added confidence in code correctness but increased token usage significantly. --- End of evaluation.