MCP server for industrial equipment: specs, comparison, identification, certificates. Equipment intelligence.
All 7 tools suffer from severely underdeveloped schemas and descriptions. Every tool accepts a single generic 'input' parameter (type: string, description: 'Input data (JSON)') with no structure, validation, or guidance. Descriptions are 1 - 3 words, far below the 10 - 1024 character baseline (avg 194 chars for A+ tools). No output schemas documented. No error handling guidance. Tools are thin proxies to a remote Bankr API with no value-add validation, pagination, or result shaping. This server reads as a proof-of-concept, not production-ready.
Certificate validator
Equipment comparison
Equipment identification
Equipment specifications database
Industrial spec extractor
Product classifier
Specification extractor
All tools accept a single generic 'input' parameter (type: string) with no schema structure. LLMs cannot infer what JSON structure to pass, forcing trial-and-error or external documentation lookup.
Tool descriptions are 1 - 3 words ('Equipment comparison', 'Certificate validator'). Baseline for A+ tools is 50 - 200 chars. No guidance on WHEN to use each tool, WHAT it returns, or HOW to structure the input JSON.
No output schema documented. Tools return JSON.stringify(result) as plain text. LLMs cannot parse structured fields, extract IDs for chaining, or plan downstream calls.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 25 | 2026-07-28+ | v2 |
No error handling or recovery guidance. callBankr() catches exceptions and returns {status: 'error', error: e.message}. LLMs receive raw error text with no actionable next steps or retry classification.
Hardcoded wallet address (0xc521cc3eae2a6a3cde11c8bde0b46006d6847360) and base URL in source code. No environment variable injection, no secret management, no audit trail of who called what.