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dxpert-ai

dxpert: Industrial AI Agents for Manufacturing (OEE, Maintenance, Root Cause)

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TDQS

A4.4/5.0

Scored across 7 tools

Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness3/5

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.

Maintenance

ActivityNo data
ResponsivenessNo issues