Loading ToolBench
MCP server that exposes product search, comparison, solutions lookup, and personalized recommendations for brand-specific agentfronts. Implements JSON-RPC 2.0 protocol over HTTP.
Five tools with complete schemas and basic descriptions, but significant gaps in parameter documentation and error handling. All tools are READ_ONLY (good for safety), but descriptions are generic and lack LLM-optimized guidance. Parameter descriptions exist but are minimal (avg ~40 chars, below 72-char baseline). No output schemas documented. No error recovery guidance. Naming is clear (verb_noun pattern) but descriptions don't explain WHEN to use each tool or dependencies between them.
Side-by-side comparison of two or more products by id.
Return full details for a single product by id.
List solutions by category.
Personalized product recommendation from a short preference description.
Search products by category, tier, price, or keyword.
Output schemas not documented. LLMs cannot plan downstream calls or extract required fields without knowing what each tool returns.
Parameter descriptions are minimal (avg ~40 chars vs 72-char baseline). E.g., 'Product category filter' lacks guidance on valid values, format, or when to use category vs query.
No error handling or recovery guidance. If search_products returns empty, LLM has no hint to try broader query or different category.
Tool descriptions lack WHEN-to-use context. 'Search products by category, tier, price, or keyword' doesn't explain when to call search_products vs recommend_product vs get_solutions.
Inferred effective spec: 2026-07-28+.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 57 | 2026-07-28+ | v2 |
No pagination or result-limit guidance. If search_products returns 500 items, LLM context explodes. No limit parameter visible in schema.