NexTool MCP for GLPI

Get asset

glpi_get_asset

Read-onlyIdempotent

Retrieve one GLPI asset by asset type and ID, returning essential or all fields in JSON or Markdown. Use it for quick asset lookups; for hardware details, call the asset-details tool.

Instructions

Retrieve a single asset by type and ID. For hardware detail use glpi_get_asset_details.

Input Schema

TableJSON Schema

Name	Required	Description
`fields`	No	Field set: 'essential' (default, trims internal GLPI fields) or 'all'
`format`	No	Output format: 'json' (default) or 'markdown' (a table for listings, key/value lines for one item; returned in both the text and the structured result)
`assetId`	Yes	Item ID
`asset_type`	Yes	Asset itemtype, e.g. Computer, Monitor, Printer, NetworkEquipment, Software
`expand_dropdowns`	No	Expand dropdown IDs to names

Output Schema

TableJSON Schema

Name	Required	Description
`data`	No
`note`	No	Pagination hint: how to fetch the next page
`count`	No	Items in this page
`format`	No	'markdown' when data holds the markdown rendering

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.5.2Oct 5, 2026

TDQS

A4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is fully covered. The description adds only the sibling routing, disclosing no further behavioral traits such as auth requirements, error behavior, or the essential/all field trimming effect on results.

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?

Two short sentences, front-loaded with the core action and followed immediately by the disambiguation rule. Zero filler.

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?

An output schema exists, so return values need no explanation, and the sibling for detail is named. Complete for the retrieval task, though it omits any note on what the asset ID refers to across types or the effect of expand_dropdowns on output.

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

Parameters3/5

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

Schema description coverage is 100%, and the schema richly documents all five parameters including enums and the fields/format semantics. The description adds no parameter meaning beyond what the schema provides, so it sits at the baseline.

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?

States a specific verb (Retrieve), resource (asset), and scope (single, by type and ID). It also names the sibling glpi_get_asset_details and when to prefer it, so an agent can distinguish the two without opening either 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?

Explicitly routes hardware-detail lookups to glpi_get_asset_details, giving a clear condition that selects the alternative. It stops short of stating when-not to use this tool for non-hardware cases, but the guidance is concrete 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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