glpi-mcp

Search the knowledge base

glpi_search_knowledgebase

Read-onlyIdempotent

Search GLPI knowledge base articles by title to find relevant guidance, then retrieve a selected article for full details.

Instructions

Find knowledge base articles whose title matches the text. Read one with glpi_get_item (itemtype KnowbaseItem).

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": {
      "query": "<query>"
    },
    "name": "glpi_search_knowledgebase"
  }
}

Input Schema

Name	Required	Description
`limit`	No
`query`	Yes
`start`	No	Offset for pagination

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 4, 2026

TDQS

A3.6/5.0

Behavior3/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The one genuinely additive behavioral fact is that matching is on titles only, not article bodies, which sets expectations about recall; beyond that it adds no return-shape or pagination behavior.

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 short sentences, front-loaded with the action and followed by the natural next step; nothing is wasted. The phrasing is slightly compressed ('whose title matches the text') where a few more words would remove ambiguity about case or partial matching.

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 simple read-only search with no output schema, this covers what the tool finds and how to retrieve a full record. It omits what a result row contains (IDs, itemtype) and whether matching is substring or exact, which an agent must learn by calling it.

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 coverage is 33%, and the description compensates for the most important parameter by explaining that 'query' matches against article titles. The limit and start parameters get no attention beyond the schema's pagination note on 'start', and the title-only matching constraint is easy to miss.

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?

The description names a specific verb and resource ('Find knowledge base articles') and narrows the scope to title matching, which is more precise than the tool name alone. It does not differentiate itself from the sibling glpi_search_items, leaving the agent to infer why a dedicated knowledge-base search exists.

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?

It gives an explicit follow-up path: read a result with glpi_get_item using itemtype KnowbaseItem, which is exactly the guidance needed after a search. It stops short of stating when to prefer this over glpi_search_items or when not to use it at all.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server