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dxpert-ai

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

Official

add_agents

Add agents to an existing dxpert site subscription, charging the prorated fee via Stripe and updating account entitlements. Requires an account login token to approve the spend.

Instructions

Add agents to an EXISTING site subscription. THIS MOVES MONEY: it charges the prorated amount through Stripe right away and updates the account's entitlements. Each agent is $100/mo on top of the $200/mo API base; see https://dxpert.ai/store. "architect" (Namespace Architect) is COMING SOON and is refused here with 409 product_coming_soon; the all-agents bundle is how an account gets it.

Because it spends, it requires "account_token" - a human-held account LOGIN token from POST /api/account/login, or localStorage.dxpert_token after signing in at dxpert.ai. The runtime API key alone is deliberately not enough to spend money. If you do not already hold an account token, stop and ask the user for one; do not go looking for credentials.

Before calling, state the site, the agents, and the resulting monthly total, and get the user's explicit go-ahead. Call get_storefront first if you are unsure what the site already has. For a site with no subscription yet, use start_agents_purchase instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentsYes
site_nameYes
account_tokenYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so: it discloses that money moves immediately via Stripe at a prorated amount, that entitlements are updated, the per-agent and base pricing, and a specific failure mode (409 product_coming_soon for architect). It also explains why the runtime API key is insufficient, which is critical behavioral context for a spending operation.

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 the core action and the money warning in the first clause, then prerequisites, then the pre-call checklist. Slightly heavy with capitalization emphasis credentials)., but nearly every sentence carries information an agent needs.

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

Completeness4/5

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

For a paid mutation tool with no annotations and no output schema, coverage of triggers, auth, errors, alternatives, and the confirmation ritual is strong. What is not described is the shape of the success response after charging (invoice, updated entitlements), which an agent confirming the outcome would want.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It explains account_token in depth (origin endpoint, localStorage equivalent, why it exists) and implies the cost semantics of agents, but site_name is left to inference and the agent enum values are only in the schema.

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 with scope: adding agents to an EXISTING site subscription. It explicitly distinguishes itself from start_agents_purchase (no subscription) and remove_agents, so an agent can route correctly without opening sibling schemas.

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?

It names the precise preconditions (existing subscription, human account token), the alternative for the no-subscription case (start_agents_purchase), and the verification step (call get_storefront first if unsure). It also prescribes the confirmation workflow before calling.

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