GLPI MCP

by USONDIG

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

stats_by_priority

Count open GLPI tickets grouped by priority to help AI assistants assess workload and prioritize support queues.

Instructions

Retourne le nombre de tickets ouverts par priorité.

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_priority"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 28, 2026

TDQS

A3.8/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the return value ('number of open tickets by priority') without clarifying output format, whether historical data is included, or whether the operation is read-only. While the verb 'Retourne' implies reading, this is minimal disclosure for a tool with no annotation support.

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, compact sentence that directly states the tool's purpose without redundancy. It is front-loaded with the action and the key output details, earning full marks for conciseness.

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 tool with no parameters and an output schema available, the description adequately states the core functionality. It could be more complete by mentioning the return format (e.g., mapping of priority to count), but the presence of an output schema reduces the need for such detail. The description is sufficient for a simple stats query.

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, and the input schema confirms this with 100% coverage. The description adds no parameter details because there are none to document, so the baseline of 4 is appropriate.

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 a specific verb ('Retourne' = returns) and resource ('le nombre de tickets ouverts par priorité' = number of open tickets by priority). It distinguishes itself from sibling stats tools like stats_by_status, stats_by_type, and stats_by_category by explicitly naming the aggregation dimension (priority).

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

Usage Guidelines3/5

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

The description implies its use for querying ticket counts grouped by priority, and the sibling tool names provide context for when to choose this over other stats tools. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites.

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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