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A comprehensive Retrieval-Augmented Generation (RAG) system with MCP server integration, featuring ChromaDB vector storage, multi-LLM support (Gemini/Ollama), Redis caching, context engineering, and agentic reasoning capabilities.
Five tools with basic descriptions and partial schemas. Tool names follow verb_noun convention (rag_query_tool, semantic_search, list_documents, cache_stats, flush_cache). Descriptions exist but are brief (34-89 chars, below the 194-char baseline). Input schemas present for most tools with type definitions, but lack depth in parameter descriptions and output schema documentation. No error handling guidance, no pagination support, no security annotations. Composition is reasonable (single responsibility per tool), but parameter descriptions are minimal and output structures are not formally documented.
Return Redis cache statistics.
Flush all cached RAG answers from Redis.
List all documents currently indexed in ChromaDB.
Answer a question using RAG (ChromaDB retrieval + LLM generation + Redis cache).
Search indexed documents and return matching chunks without LLM generation.
Output schemas not documented. Tools return data but LLMs cannot infer response structure (fields, types, pagination). Forces agents to guess what data is available.
Parameter descriptions are minimal (1-2 words). 'Number of top results to retrieve' is adequate, but 'Search mode: chunk, page, or both' lacks guidance on when to use each mode. LLMs cannot reason about parameter selection.
No error handling guidance. Tools do not document what errors can occur, whether they are retryable, or what the LLM should do next. E.g., what if ChromaDB is unavailable? What if Redis cache fails?
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 47 | <=2025-11-25 | v2 |
No pagination or result limits documented. semantic_search and rag_query_tool accept top_k but do not specify max value or whether results are paginated. Large result sets could exhaust context window.
flush_cache is a destructive operation (WRITE risk) with no confirmation step or dry-run option. Agents could accidentally clear cache without intent.