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GLPI MCP

GLPI MCP

by USONDIG
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

stats_by_assignee

Counts tickets assigned to each technician, helping you monitor support workload and identify uneven assignments in GLPI.

Instructions

Retourne le nombre de tickets par technicien assigné.

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": "stats_by_assignee"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It only states the basic result (count per assignee) but does not mention whether it includes all tickets regardless of status, how zero-count technicians are handled, or any other behavioral nuances. This is minimal transparency.

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?

The description is a single, concise sentence that immediately conveys the tool's purpose. There is no wasted wording, and it is efficiently front-loaded with the action and resource.

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?

Given the tool's low complexity (no parameters) and the presence of an output schema, the description sufficiently covers what the agent needs to know to invoke the tool. It explains the core functionality without needing to elaborate on return values, as those are defined by the output schema.

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?

The tool has zero parameters, so the baseline is 4. There is no parameter information to add, and the description correctly focuses on the tool's output rather than input, making it appropriate for a no-parameter tool.

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 description clearly states the specific verb 'retourne' and the resource: the number of tickets per assigned technician. It distinguishes itself from sibling stats tools like stats_by_priority and stats_by_status by focusing specifically on assignee.

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?

The description provides no guidance on when to use this tool versus alternatives. It simply states what it does without any context about use cases, prerequisites, or exclusions. The agent must infer its usage solely from the tool name.

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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