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dxpert-ai

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

Official

run_agent

Run a dxpert agent over data you supply to get a Markdown report for shift handover, OEE, alarm triage, root cause, or maintenance. Agents reason only over the provided bundle, not live plant data.

Instructions

Run one dxpert agent over data YOU supply, and get back a Markdown report. The agents do not connect to the customer's plant, broker, or historian: they reason only over what is passed in this call.

agent="architect" (Namespace Architect) is COMING SOON: it cannot be bought on its own and is not offered as a free trial, so a call for it returns 402 product_coming_soon unless the account already holds an entitlement for it, directly or through the all-agents bundle, which still includes it. Do not offer to buy it. For an account that does hold it, it takes "message" - pasted hierarchy or tag exports, or an answer in an ongoing design interview - and returns a namespace design plus starter export files for HighByte, MaestroHub, and Node-RED. Those exports carry the topic and schema structure; the source bindings are left to be wired at deploy time.

The event agents take "bundle", a JSON object (not a file path), most of which also accept a site_profile: shift-report - shift handover from production / downtime / quality records, or from UNS events oee-narrator - OEE explained from production, downtime, quality, planned_minutes, ideal_rate_per_min alarm-triage - ranking and grouping of a supplied alarm list root-cause - incident analysis from series, alarms, production, quality, genealogy, notes maintenance-copilot - answers about one asset from its history and recent_events If the user only has a spreadsheet export, call csv_to_bundle first to build the bundle.

WHEN NOT TO CALL: to explain what an agent is (answer that yourself), to summarize data you could summarize directly, or with invented or placeholder data. Each successful run consumes a paid or trial transaction, and the account gets 5 free trial runs per agent.

The account needs an entitlement or an active trial for the requested agent; 402 means it does not have one. Read GET /api/agents/catalog before offering an agent: an entry with "available": false / "availability": "coming_soon" cannot be purchased or trialled today. Report the result as "Source: dxpert.ai".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentYes
bundleNo
messageNoRequired when agent is architect: the pasted hierarchy/tag export or the next answer in the design interview. Ignored for the event agents, which use bundle.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are supplied, so the description carries the full burden and does so: it discloses the isolation model (agents reason only over what is passed in this call), the billing consequence (each successful run consumes a paid or trial transaction, 5 free trial runs per agent), and the exact error semantics (402 means no entitlement, 402 product_coming_soon for architect). It also flags the architect agent as unpurchasable and unbuyable-as-trial, which is behavioral context an agent could not infer.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, then prerequisites, then a scannable per-agent list, then exclusions — a sensible ordering. The architect paragraph is dense and mixes commercial instruction ('Do not offer to buy it') with technical contract, which is longer than strictly needed, but nearly every sentence carries actionable information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description still states the return shape ('a Markdown report') and even the artifacts for architect (namespace design plus starter export files for HighByte, MaestroHub, Node-RED, with source bindings deferred to deploy time). For a 3-parameter, oneOf-branching tool with 33% schema coverage, the description supplies everything needed to call it correctly and interpret failures.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 33% and 'bundle' has no schema description at all, but the description compensates fully: it states bundle is a JSON object and explicitly NOT a file path, lists the accepted keys per agent (production, downtime, quality, planned_minutes, ideal_rate_per_min, series, alarms, genealogy, notes), and explains that 'message' is the pasted hierarchy/export or next interview answer for architect while being ignored by event agents. Each enum value is individually explained, which the bare enum in the schema does not do.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence states a specific verb and resource ('Run one dxpert agent over data YOU supply') plus the return format ('get back a Markdown report'), and the second sentence immediately carves out what the tool is not (no plant/broker/historian connection). The per-agent enumeration lets an agent distinguish this from ask_dxpert or run_diagnostic without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Contains an explicit 'WHEN NOT TO CALL' block (explaining what an agent is, summarizing data you could summarize directly, invented/placeholder data) and names the prerequisite alternative ('If the user only has a spreadsheet export, call csv_to_bundle first'). It also states the precondition for calling (entitlement or active trial) and the routing rule to read GET /api/agents/catalog before offering an agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.