dxpert: Industrial AI Agents for Manufacturing (OEE, Maintenance, Root Cause)
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TDQS
Scored across 7 tools
Each tool targets a distinct action (runtime manifest, storefront state, advisory chat, agent run, diagnostic, CSV conversion, purchase), and descriptions include explicit WHEN TO CALL / WHEN NOT TO CALL guidance. The main soft spot is the cluster of 'run/produce a report' tools — ask_dxpert, run_agent, and run_diagnostic overlap conceptually (advisory vs. agent vs. readiness scoring) though the descriptions do distinguish them well.
Most tools follow a verb_noun snake_case pattern (get_runtime_manifest, get_storefront, ask_dxpert, run_agent, run_diagnostic, start_purchase). 'csv_to_bundle' is a noun_to_noun converter name that breaks the verb-first convention, but overall readability and consistency are strong.
Seven tools is well-scoped for a multi-capability server covering advisory, agents, diagnostics, conversion, and purchases. Each tool earns its place with no redundant entries and no obvious padding.
The surface covers the core workflows (ask, run agent, diagnose, convert, purchase, check plan/runtime), but descriptions repeatedly instruct the agent to READ GET /api/catalog and GET /api/agents/catalog for live prices and agent availability, yet no tool exposes those endpoints. This forces the agent to quote from memory or guess availability, a notable gap in the advertised flow.