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EQVPS — VPS and code sandboxes for AI agents

Open a support ticket

create_ticket

Open a support ticket with EQVPS. subject and message are required; optional service_id ties it to one of your VPS. The response includes the ticket code — use that with get_ticket / reply_ticket / close_ticket. Requires: token.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYes
subjectYes
service_idNoService code as returned by list_vps/order_vps, e.g. "68J67T5" (a plain numeric id is also accepted).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • addedInput schema / properties / service_id / description
      Added value: +"Service code as returned by list_vps/order_vps, e.g. \"68J67T5\" (a plain numeric id is also accepted)."
    • removedInput schema / properties / service_id / exclusiveMinimum
      Removed value: -0
    • addedInput schema / properties / service_id / minLength
      Added value: +1
    • changedInput schema / properties / service_id / type
      Previous value: -"integer"New value: +"string"
  2. Added

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose two valuable traits: the auth requirement ("Requires: token") and the fact that the response returns a `code` needed by other tools. It does not cover failure behavior, rate limits, or whether a created ticket is editable/deletable via close_ticket.

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

Conciseness5/5

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

Four short clauses, no filler: action, required/optional params, downstream workflow, and auth prerequisite. The follow-up tool chain is front-loaded right after the parameter summary, which is exactly what an agent needs next.

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 3-param creation tool with no annotations and no output schema, it covers auth, required inputs, and the key return value (the ticket code). The only gap is the absence of information about what a ticket looks like or how service_id mismatches are handled.

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?

With schema coverage at 33%, the description must compensate, and it does: it labels `subject` and `message` as required and explains that `service_id` is optional and links the ticket to a specific VPS. The schema supplies the service code format example, so the pair together is nearly complete.

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?

Specific verb + resource (open a support ticket) with the provider named, and it distinguishes itself from siblings by naming get_ticket, reply_ticket, and close_ticket as the downstream lifecycle tools. An agent can place this tool in the ticket workflow 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 Guidelines4/5

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

It states the required fields and that optional service_id ties the ticket to a VPS, and it explicitly routes the agent onward using the returned code. There is no explicit when-not-to-use or alternative (e.g., list_tickets for reading existing tickets), but the usage context is clear and actionable.

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

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