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MCP RAG Agent for e-commerce product search and retrieval using semantic embeddings and FAISS vector indexing
RAG-MCP has 3 tools with significant quality gaps. Tool names lack clear action verbs and are verbose/unclear. Descriptions exist but are minimal and lack actionable guidance. Input schemas are partially visible but lack proper type documentation and parameter descriptions. No output schemas are documented. Error handling is absent, tools return generic exception strings. The server appears specialized for a RAG use case but lacks the clarity and structure required for LLM-driven tool selection.
Pretty print a list of ProductResponse objects' metadata
Returns a dictionary of unique product attributes and sub-attributes that can be used by an LLM to refine, tune, or rerank search results based on user intent. Provides metadata that guides the LLM in determining which fields are important to filter or re-rank products when search results do not return exact semantic matches.
Reorders a list of ProductResponse objects based on a provided ranking of indices. Maps ranked indices back to the original list of ProductResponse objects and returns them in the ranked order.
Tool naming lacks clarity and violates verb-noun convention. 'preety_print_product_metadata_response' (typo + vague), 'return_ranked_product_response_from_ranked_index' (verbose, 58 chars vs baseline 18), and 'product_metadata_analysis_for_refine_or_tuning_search_result' (60 chars, uses conjunction) make it difficult for LLMs to infer intent.
Input parameter schemas lack validation constraints. 'ranked_indices: List[int]' has no bounds checking documented. LLM could pass out-of-bounds indices (e.g., [0, 10, 2] for a list of 3 items), causing runtime IndexError with no recovery guidance.
No output schemas documented for any tool. LLMs cannot plan downstream tool calls or know what fields to extract. E.g., 'preety_print_product_metadata_response' returns 'str', but does not specify format (JSON, markdown, CSV?). 'return_ranked_product_response_from_ranked_index' returns List[ProductResponse], but the schema of ProductResponse is not shown.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 40 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 38 | - | v1 |
Error handling is absent or minimal. 'preety_print_product_metadata_response' catches exceptions and returns 'Error formatting product response: {str(e)}...', a generic message that does not tell LLM what to do next. No distinction between retryable errors (JSON parse) and fatal ones (invalid ProductResponse).
Parameter descriptions are vague or missing domain context. E.g., 'product_response_list' description ('Either a List[ProductResponse], a JSON string, or a list of JSON strings') lists types but does not explain why multiple types are accepted or how the tool disambiguates them. LLM may pass wrong type.