Official Ada Diamonds MCP server for local MCP clients: search live lab grown diamond inventory, engagement ring settings, fine jewelry, and buying guides from Claude Desktop, Cursor, and any stdio client.
Ada Diamonds MCP server has 6 tools with complete input schemas and descriptions. Naming follows verb_noun convention (search, list, read). Descriptions are present but brief (10-50 chars), below the 50-200 char LLM-optimized baseline. All tools are read-only (no state mutation). Schemas include proper types and parameter descriptions. However, output schemas are not documented, LLMs cannot predict response structure for chaining. No error handling guidance, no pagination details despite search tools returning lists. Parameter constraints (enums for shape, color, clarity, cut) are present but not exhaustive.
Search available loose lab grown diamonds
List knowledge base articles
Search fine jewelry: wedding bands, earrings, necklaces, bracelets, fashion rings
Print one knowledge base article as markdown
Search engagement ring settings
Showroom locations and remote consultation options
Output schemas not documented. LLMs cannot predict response structure (fields, types, nested objects) for downstream tool chaining or data extraction.
Tool descriptions are too brief (10-50 chars). Baseline for LLM-optimized descriptions is 50-200 chars. Current descriptions lack context on when to use each tool vs. similar ones (e.g., jewelry vs. rings).
No pagination guidance. Search tools (diamonds, rings, jewelry, guides) accept limit and offset but do not document max results, total count, or next_cursor behavior. Large result sets risk context window exhaustion.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | C | 65 | 2026-07-28+ | v2 |
showrooms tool has no input parameters documented. Schema score is 40 because parameter descriptions are missing, making it unclear what the tool returns or how to use it.
No error handling guidance. Tools do not document what happens on invalid input (e.g., invalid shape, out-of-range price), making it hard for LLMs to self-correct or retry intelligently.