NexTool MCP for GLPI

Get ticket stats

glpi_get_ticket_stats

Read-onlyIdempotent

Count GLPI tickets by status—New, Processing, Pending, Solved, Closed—and total without listing them; apply optional criteria to narrow results by entity, requester, technician, or category.

Instructions

Ticket counts per status (New, Processing, Pending, Solved, Closed) plus the total, without listing the tickets. Optional criteria narrow the scope, e.g. one entity: [{field: 80, searchtype: 'equals', value: }] (Ticket search option IDs: 80 = entity, 4 = requester, 5 = technician, 7 = category; same criteria shape as glpi_search).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "glpi_get_ticket_stats"
  }
}

Input Schema

Name	Required	Description	Default
`criteria`	No	Optional criteria to narrow the scope: array of {field, searchtype, value, link}

Output Schema

Name	Required	Description	Default
`data`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.5.2Oct 5, 2026

TDQS

A4.1/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is fully covered. The description adds the aggregate-vs-record return behavior, which is useful, but says nothing about permissions, entity visibility scope, or cost of the query.

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 return shape and the exclusion, then the optional criteria and example. Dense but every clause carries information; the parenthetical ID mapping is slightly heavy but earns its place.

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-value detail is not the description's burden, and the single optional parameter is explained. What remains unstated is default scoping (which entities are counted when criteria are omitted), which an agent would need to know to interpret results correctly.

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 100% but the schema only documents the generic {field, searchtype, value, link} shape with no meaning for field values. The description supplies the mapping that matters most (80=entity, 4=requester, 5=technician, 7=category) and a worked example, which is well beyond the structured data, though the list is partial and searchtype values are still unexplained.

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+resource ('ticket counts per status ... plus the total') and immediately delimits it against the sibling list tools by saying it works 'without listing the tickets'. An agent can distinguish this from glpi_list_tickets and glpi_search from the first clause alone.

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?

Gives a clear usage context (aggregate counting rather than record retrieval) and demonstrates optional narrowing with a concrete criteria example. It does not name an explicit alternative to prefer (e.g. glpi_count_items) or state when not to use it, so it stops short of a 5.

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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