NexTool MCP for GLPI

Create asset

glpi_create_asset

Create a new asset in GLPI by providing the asset type and fields such as name, serial, location, and user. Populate inventory records for computers, monitors, printers, and other asset types.

Instructions

Create an asset. Common fields: name, entities_id, serial, otherserial, locations_id, states_id, manufacturers_id, users_id, comment.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "asset_type": "<asset_type>",
      "input": "<input>"
    },
    "name": "glpi_create_asset"
  }
}

Input Schema

Name	Required	Description	Default
`input`	Yes	Asset fields
`asset_type`	Yes	Asset itemtype, e.g. Computer, Monitor, Printer, NetworkEquipment, Software

Output Schema

Name	Required	Description	Default
`data`	No
`replayed`	No	True when an identical recent create was returned instead of repeated

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.5.2Oct 5, 2026

TDQS

B3.3/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false and destructiveHint=false, so the write/non-idempotent/non-destructive profile is covered. The description adds only a modest hint via 'Common fields', implying field sets vary by asset type, but says nothing about type-specific requirements, auth, or failure modes.

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?

Two tight sentences, front-loaded with the operation and followed by the useful field list. No filler, though it is terse to the point of under-specification rather than wasteful.

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

Completeness3/5

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

An output schema exists, so return values need not be described. However, for a polymorphic tool spanning nine asset types with a freeform input object, the description omits which fields are required or type-specific, leaving a real gap for correct invocation.

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 coverage is nominally 100%, but the critical 'input' param is a freeform object with no documented fields, so the description's field list (name, entities_id, serial, etc.) adds genuine meaning the schema lacks. asset_type semantics (the enum) are only covered by the schema, not expanded here.

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

Purpose4/5

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

States a specific verb (Create) and resource (asset), so the operation is unambiguous. It does not differentiate itself from siblings like glpi_update_asset or glpi_list_assets, but the create semantics are clear from the verb.

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

Usage Guidelines2/5

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

There is no guidance on when to use this versus glpi_update_asset or glpi_create_document, no prerequisites, and no indication of permissions or required context. It simply says what it does, leaving all usage decisions to inference.

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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